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(1)Adam Jabłoński - Methods of Autmatized Monitoring and Diagnosis of Wind Turbines. 1/241 _____________________________________________________________________. AGH University of Science and Technology Faculty of Mechanical Engineering and Robotics Department of Robotics and Mechatronics. PhD thesis. M ETHODS. OF AUTOMAT IZE D MONIT ORING AND DIA GNOSIS OF WIND TURBINES. Author: Adam Jabłoński. Thesis supervisor: Tomasz Barszcz, PhD. Cracow 2012.

(2) Adam Jabłoński - Methods of Autmatized Monitoring and Diagnosis of Wind Turbines. 2/241 _____________________________________________________________________.


(4) Adam Jabłoński - Methods of Autmatized Monitoring and Diagnosis of Wind Turbines. 4/241 _____________________________________________________________________. This thesis is dedicated to the memory of Kim Lamey.

(5) Adam Jabłoński - Methods of Autmatized Monitoring and Diagnosis of Wind Turbines. 5/241 _____________________________________________________________________. ACKNOWLEDGEMENTS The author gratefully acknowledges the support of the supervisor, Tomasz Barszcz, Ph.D. from the AGH University of Science and Technology for making the process of writing this thesis the great adventure that it was.. The author would like to acknowledge the financial support of the Polish Ministry of Science and Higher Education under research grant no N504 670540. Niniejszym zaświadcza się, iż mgr inż. Adam Jabłoński w latach 2010-2012 był Stypendystą w ramach projektu „Doctus – Małopolski fundusz stypendialny dla doktorantów” współfinansowanego ze środków Unii Europejskiej w ramach Europejskiego Funduszu Społecznego..

(6) Adam Jabłoński - Methods of Autmatized Monitoring and Diagnosis of Wind Turbines. 6/241 _____________________________________________________________________ OUTLINE Acknowledgements ...................................................................................................... 5 1. Introduction ........................................................................................................ 10 1.1. State-of-the art of wind turbines monitoring ............................................... 10 1.1.1. Objects of wind turbine condition monitoring ..................................... 12 1.1.2. Condition monitoring techniques ......................................................... 12 1.1.3. System design and configuration ......................................................... 16 1.1.4. System performance and maintenance................................................. 18 1.2. Fields of automatization of wind turbine condition monitoring .................. 18 1.3. Justification of the work............................................................................... 21 1.4. Goal and scope of the work ......................................................................... 22 2. Wind turbines as objects of vibration-based condition monitoring .............. 28 2.1. Typical constructions of wind turbines ........................................................ 28 2.2. Power control of wind turbines .................................................................... 30 2.3. Overview of mechanical drive train elements ............................................. 31 2.4. Wind turbine characteristic frequencies....................................................... 34 2.5. Typical malfunctions and faults ................................................................... 38 3. Data preprocessing............................................................................................. 44 3.1. Elements of data acquisition in condition monitoring systems ................... 44 3.2. Criteria for correct data acquisition ............................................................. 45 3.2.1. Process parameters validation .............................................................. 45 3.2.1.1. Independent process parameters validation ................................. 46 3.2.1.2. Relative process parameters validation ........................................ 48 3.2.2. Vibration data selection ....................................................................... 48 3.2.2.1. Selection based on fixed time intervals ....................................... 49 3.2.2.2. Selection based on operational states ........................................... 50 3.2.2.3. Selection based on process parameters tracking .......................... 50 3.2.2.4. Fluctuation of non-stationary process parameters ....................... 51 3.3. Validation of system configuration – range dynamics ................................ 53 3.4. Validation of vibration signals ..................................................................... 58 3.4.1. Need for on-line signal validation........................................................ 60 3.4.2. Literature review on signal validation ................................................. 61 3.4.3. Classifications of vibration signals ...................................................... 63 3.4.4. Features of correct and incorrect vibration signals .............................. 64 3.4.5. Examples of correct and incorrect vibration signals ............................ 64 3.4.6. Methods for amplitude-based validation of vibration signals.............. 67 3.4.6.1. Minimum Energy Rule ................................................................ 67 3.4.6.2. N-point rule .................................................................................. 68 3.4.6.3. Z-point rule .................................................................................. 71 3.4.6.4. U-point rule .................................................................................. 73 3.4.6.5. Amplitude range dynamics rule ................................................... 74 3.4.6.6. Statistical rules ............................................................................. 76 3.4.6.7. Comparative signal assessment methods ..................................... 80 3.4.7. Software implementation remarks ....................................................... 87 3.5. Determination of machine operational states ............................................... 87 3.5.1. Algorithm presentation ........................................................................ 89 3.5.1.1. Signal assumptions....................................................................... 89 3.5.1.2. Data clustering ............................................................................. 89 3.5.1.3. Outliers removal........................................................................... 91.

(7) Adam Jabłoński - Methods of Autmatized Monitoring and Diagnosis of Wind Turbines. 7/241 _____________________________________________________________________ 3.5.2. Case study ............................................................................................ 93 3.5.2.1. Outliers removal........................................................................... 93 3.5.2.2. Initial selection of number of states – hierarchical clustering ..... 94 3.5.2.3. Clustering – k-means and mixture Gaussian model methods ...... 95 3.5.2.4. Indicator – initial states verification ............................................ 97 3.5.2.5. Modification of cluster range ....................................................... 97 3.5.2.6. Selection of input set for states definition.................................... 98 4. Signal features extraction ................................................................................ 102 4.1. Decomposition of vibration signals ........................................................... 102 4.1.1. Signal decomposition benefits ........................................................... 103 4.1.2. Separation methods overview ............................................................ 103 4.1.3. Proposition of a frequency-domain decomposition technique........... 104 4.1.3.1. Max-med estimator as alternative to kurtosis-based estimators 104 4.1.3.2. Removal of deterministic components....................................... 105 4.1.3.3. Algorithm description ................................................................ 106 4.1.4. Case study .......................................................................................... 108 4.2. Narrowband envelope analysis .................................................................. 113 4.2.1. Problem background .......................................................................... 116 4.2.2. Selection of the optimal bandwidth ................................................... 117 4.2.3. Selection of the optimal center frequency ......................................... 121 4.2.4. Methods for optimal freqeuncy band selection (OFB) ...................... 123 4.2.4.1. Direct spectrum comparison ...................................................... 124 4.2.4.2. Spectrogram ............................................................................... 124 4.2.4.3. Spectral kurtosis ......................................................................... 125 4.2.5. Band selection optimilization criteria ................................................ 129 4.3. Protrugram as a novel frequency band selection method .......................... 129 4.3.1. Method description ............................................................................ 129 4.3.2. Case studies ........................................................................................ 132 4.3.2.1. Simulated signal ......................................................................... 133 4.3.2.2. Test rig case study...................................................................... 142 4.3.3. Selection of Protrugram step size ...................................................... 144 4.3.4. Method extention – utilization of statistical estimators ..................... 145 4.3.4.1. Description of extension algorithm ............................................ 145 4.3.4.2. Algorithm steps .......................................................................... 146 4.3.4.3. Algorithm block diagram ........................................................... 147 4.3.5. Protrugram summary ......................................................................... 149 4.4. Instantaneous Circular Pitch Cyclic Power (ICPCP) – a novel tool for diagnosis of planetary gearboxes ........................................................................... 149 4.4.1. Characteristics of wind turbine planetary gearbox ............................ 149 4.4.2. Overview of PG diagnosticmethods .................................................. 151 4.4.3. Method Description ........................................................................... 151 4.4.4. Comparison with other methods ........................................................ 155 5. Selection and automatization of diagnostic methods .................................... 158 5.1. Data acquisition ......................................................................................... 158 5.1.1. Reference stage .................................................................................. 159 5.1.2. Continuous monitoring stage ............................................................. 160 5.1.2.1. A complete algorithm for data acquisition in wind turbines continuous condition monitoring ................................................................... 163 5.1.3. Hardware realization .......................................................................... 166 5.1.3.1. Parallel data acquisition ............................................................. 166.

(8) Adam Jabłoński - Methods of Autmatized Monitoring and Diagnosis of Wind Turbines. 8/241 _____________________________________________________________________ 5.1.3.2. Data acquisition time lag ........................................................... 166 5.1.4. Case study .......................................................................................... 167 5.2. Determination of alarm levels .................................................................... 168 5.2.1. Threshold setting requirements .......................................................... 170 5.2.2. Probability distributions for threshold setting.................................... 171 5.2.2.1. Weibull probability distribution ................................................. 172 5.2.2.2. Generalized extreme value probability distribution ................... 173 5.2.2.3. Extreme Value probability distribution ..................................... 174 5.2.2.4. Inverse Gaussian probability distribution .................................. 175 5.2.3. Description of real data ...................................................................... 176 5.2.4. Threshold setting procedure ............................................................... 179 5.2.4.1. Symmetrical approach ............................................................... 179 5.2.4.2. Positive amplitude approach ...................................................... 180 5.2.4.3. The method flowchart ................................................................ 181 5.2.4.4. Data preprocessing ..................................................................... 181 5.2.4.5. Distribution model fitting .......................................................... 183 5.2.4.6. Setting the reference and threshold values................................. 184 5.2.5. Case study – optimized fits on the real datasets ................................ 185 5.2.5.1. Distribution function fit assessment........................................... 185 5.2.5.2. Comparison of selected probability distributions ...................... 186 5.3. Development of referential data................................................................. 190 5.4. Diagnostic reports ...................................................................................... 193 5.4.1. Tools for reports creation ................................................................... 193 5.4.2. Reports interface ................................................................................ 196 6. Diagnostic Center ............................................................................................. 202 6.1. Introduction ................................................................................................ 202 6.1.1. Diagnostic Center - architecture ........................................................ 202 6.1.2. Realization of data access within condition monitoring systems ...... 204 6.2. Integration with Matlab environment ........................................................ 206 6.2.1. Integration scheme ............................................................................. 206 6.2.2. Example of application ...................................................................... 209 7. Summary ........................................................................................................... 212 7.1. Results ........................................................................................................ 212 7.2. Prospects for industrial implementation .................................................... 213 7.3. Future works .............................................................................................. 213 Literature .................................................................................................................. 214 Curriculum Vitae ..................................................................................................... 232 Appendix A ............................................................................................................... 234.


(10) Adam Jabłoński - Methods of Autmatized Monitoring and Diagnosis of Wind Turbines. 10/241 _____________________________________________________________________. 1. INTRODUCTION 1.1. State-of-the art of wind turbines monitoring Rapid growth of total wind power generation capacity in last decade has restated wind turbine (WT) condition monitoring (CM) demands. On one hand, large number of wind turbines concentrated in so-called "wind farms" requires novel solutions related to condition monitoring systems' (CMS) architecture. On the other hand, advances in digital signal processing techniques in parallel with progress of available information technology (IT) solutions compel manufacturers of CMS to provide a new quality of machine diagnosis concerning fault detection, fault identification, and remaining life prediction. Consequently, research and development (R&D) actions within wind turbines' CMS generally emerge as large, high-budget, interdisciplinary, frequently international projects with additional governmental support. Nevertheless, individual, solid scientific studies resulting in novel signal processing techniques continuously serve as modern CMS’ driving force. A complete scope of wind turbine condition monitoring covers many aspects of technology, engineering, and science, as illustrated in Figure 1.. Figure 1. Aspects of wind turbine condition monitoring.

(11) Adam Jabłoński - Methods of Autmatized Monitoring and Diagnosis of Wind Turbines. 11/241 _____________________________________________________________________ In this scheme, technology covers hardware and software solutions for data acquisition, storage, transfer, and visualization. Engineering part includes realization of hardware and software solutions and irreplaceable diagnostic experience. As illustrated in Figure 1, these two aspects directly impact the overall system performance, i.e. proper flow of proper data. Last but not least, science input concentrates in data analysis and data acquisition algorithms, additionally covering common fields of feature extraction and system architecture. The merge of science and engineering illustrated in Figure 1, which enables effective extraction of characteristic features from signals, requires thorough knowledge of wind turbine’s drive train kinematics and advanced signal processing, especially for machinery working under variable operational parameters such as wind turbines. In this case, advanced signal processing techniques enable fault detection in their incipient stage, accurate fault identification and possible fault development prediction. The second science common field, namely part of system architecture based on modern information technology (IT) solutions requires advanced analysis of referential data and current data tracking for proper system configuration and adaptation.. Figure 2. General stages of realization of wind farm condition monitoring. From the realization-point-of-view, a process of complete condition monitoring of wind farms might be decomposed into four consecutive stages, as shown in Figure 2. Selected developments concerning architecture and technologies of modern distributed CMS are described in [19]..

(12) Adam Jabłoński - Methods of Autmatized Monitoring and Diagnosis of Wind Turbines. 12/241 _____________________________________________________________________. 1.1.1. Objects of wind turbine condition monitoring As illustrated in Figure 2, wind turbines require special attention in terms of selection of objects of monitoring and selection of diagnostic methods. Within a dominant WT type currently used worldwide, i.e. the three blade up-wind variable speed turbine with double feed asynchronous generator [131], following elements might undergo monitoring process: - blades, - mechanical drive train components (rotor, parallel and planetary gearboxes, generator structure and windings, bearings), - tower.. 1.1.2. Condition monitoring techniques Based on recent publication of Marquez et al. [84], a summary of currently applied WT diagnostic techniques is listed in Table 1. Apart from the object-based classification and input signal type classification, references listed in Table 1 are supplied with applied core signal processing operations, including: - statistical methods, - time-domain methods, - cepstrum analysis, - Fast Fourier Transform (FFT)-based methods, - demodulation techniques, - wavelet-based methods, - Hidden Markov models, - other novel techniques..

(13) Adam Jabłoński - Methods of Autmatized Monitoring and Diagnosis of Wind Turbines. 13/241 _____________________________________________________________________ Table 1. Summary of condition monitoring methods applied in WT diagnostics Blades. Rotor. Gearbox. Statistics: [88].. Statistics:[60].. Statistics: [58].. Wavelets: [102].. Cepstrum: [58],[226].. Time-domain: [83],[142],[31].. Other: [90].. Generator. Cepstrum: [59],[57].. Cepstrum: [59],[57],[214],[43].. FFT: [104].. FFT: [67].. Demodulation: [100],[17],[14],[12],[65].. Demodulation: [140],[33].. Wavelets: f[133],[161],[102],[156].. Wavelets: [210], [130],[40].. Acoustic emission. Ultrasonic techniques. Wavelets:[146].. Tower. Statistics: [58],[76],[190],[24],[203].. Vibration. Statistics: [5],[42],[212].. Bearings. Hidden-Markov: [157].. Other: [149],[64],[148],[174], [132],[38],[229],[37]. Statistics: [41].. Other [201],[30],[192],[10]. Statistics: [195],[125].. Wavelets: [40]. Other: [90],[187],[91], [47],[124],[123], [73],[138] Statistics: a[168].. Time-domain: [129]. Other: [206]. Other: [193],[99].. Wavelets: f[61]. Other: [91],[122],[121], [167]. Statistics: [41].. [163].. Statistics: [199].. Statistical: [185].. [191].. Oil analysis Time-domain:[126] Cepstrum:[213]. Strain. Electrical effects. [139].. Other: [90],[151],[187], [204]. [182],[136].. Statistical: [56]. Shock Pulse methods. Wavelets: [85]. Other: [194],[225]. Statistical:[224].. Statistical: [152]. Performance monitoring. [187]. Timedomain:[70].. Radiographic inspections. [121],[79].. Thermography. [179],[185],[41].. Other. [41],[155],[71].. Process parameters. [224].. [224]. [71],[166],[96].. [198]..

(14) Adam Jabłoński - Methods of Autmatized Monitoring and Diagnosis of Wind Turbines. 14/241 _____________________________________________________________________ As illustrated in Table 1, vibration-based methods constitute major part of methods employed in WT monitoring, especially for rotating elements [102]. Historically, position, velocity, and acceleration sensors were applied to collect data in lowfrequency region, middle-frequency region, and high-frequency region, respectively. However, modern digital technology enables relatively quick calculation of velocity and position signals from accelerations signals as well. A typical, vibration-based WT CMS consists of accelerometers located at main bearing, individual gearboxes, and generator plus a phase marker, i.e. reference speed sensor.. The acoustic emission (AE)-oriented methods are based on analyzing high frequency transient elastic waves generated by the sudden release of energy due to strain or damage within or on the surface of a solid material or by the interaction of two media in relative motion [138]. Main advantages of AE methods include high signal-to-noise ratio (SNR) and dependence on material characteristics rather than machine rotational speed [206], which is an important feature regarding machinery working under nonstationary operational parameters, such as wind turbines. Main disadvantages of AE techniques are caused by physical properties of acoustic waves, namely attenuation and deflection. Since any acoustic wave travels from the signal source in all directions, it reduces its amplitude together with the distance from the source. The reflection phenomenon is caused by AE sensitivity to interfaces such as changes in material. Taking into account the last characteristics, the sensor placement (i.e. microphone) is of utmost importance within AE condition monitoring.. Ultrasonic techniques are typically constrained to structural monitoring of WT blades with a focus on detection of surface and subsurface defects. Generally, such methods produce images, which help in detection of delaminations, lack of glue, with a possible additional information on size and geometry of the fault [167]. The oil analysis, frequently referred as to “oil debris analysis” are based on monitoring of the oil temperature and contamination caused by excessive amount of debris in oil. This method, originally designed for detecting impending failure of bearings and gears in gas turbine engines [163] and helicopter gearboxes, has been proved to be useful for WT condition monitoring as well [199]. One of main advantages of this technology is it simplicity; a diagnostic information is based on.

(15) Adam Jabłoński - Methods of Autmatized Monitoring and Diagnosis of Wind Turbines. 15/241 _____________________________________________________________________ reading of wear debris released form failing gears or bearings captured by a magnetic sensor located in machine oil system.. Strain measurements applied in WT monitoring, such as reviewed in [151] are usually dedicated to blades assessment. The simplest applied technique is based on deformation sensors, which locate peak structural strains due to high stress levels. Other promising techniques utilize optical fiber sensors; however, they are still at the developing stage.. Electrical effects-based WT condition monitoring refers to assessment of technical state of motors and generators. Main techniques include spectral analysis for isolation faults detection and measurement of electrical resistance for delaminations and cracks detection [182]. Furthermore, as illustrated in [191], electrical effects might be used for diagnostics of bearings; however, this technique requires additional signal postprocessing due to relatively low SNR. So-called “shock-pulse” methods are dedicated to rolling element bearing (REB) diagnostics by detection of mechanical shocks that are generated when a ball or roller in a bearing comes in contact with a damaged area of raceway or with debris [56]. In this scheme, a shock pulse transducer reacts with a large amplitude oscillation to the weak shock pulses, because it is excited at its resonance frequency (typically 32 kHz or 36 kHz). Simultaneously, machine vibrations, of a much lower frequency, are filtered out.. Typical measure process parameters of a WT include wind speed, generator speed, load, and nacelle movement signal frequently referred in literature as the “azimuth” signal. As demonstrated in [224] and [152], definition of permissible values of process parameters might be useful in detection of WT abnormal state. The author’s experience in industrial wind turbine CMS design also shows that tracking of process parameters enables monitoring of the condition monitoring system itself. Moreover, it gives an overall description of WT operation, which is useful from economical pointof-view..

(16) Adam Jabłoński - Methods of Autmatized Monitoring and Diagnosis of Wind Turbines. 16/241 _____________________________________________________________________ So called “wind turbine performance monitoring” is defined in [94] as continuous tracking of the relationship between power, wind velocity, rotor speed and blade angle and detection of large deviations (from a normal state). This technique is designed to be applied in its primitive form with additional false alarms prevention techniques.. WT diagnostic methods based on radioscopic inspections cover similar machine elements as ultrasonic techniques, namely WT blades. The basic assumption behind this technique is that in case of blade’s delaminations or crack, a variation in X-ray absorption within blade is expected. Although this concept is not very common, a practical solution utilizing X-rays for blades monitoring is illustrated in [79].. Another family of WT monitoring state-of-the methods originates from thermal phenomena; precisely, from excessive heat generation. The application of thermography might enable failure identification of electronic and electric components and also fault detection of WT drive train components. One of main application of thermography in WT condition monitoring described in [94] is detection of WT transformer’s winding problems leading to inhomogeneous temperature deviations in the phase windings. The main disadvantage of thermographical methods is that they need to undergo a mandatory visual interpretation.. Although possible to be classified elsewhere, some methods additionally to be found in [41], [155],[71],[166], and [96] show a possible path of evolution of WT condition monitoring, including infrared cameras and novel techniques based on guided waves.. 1.1.3. System design and configuration Regarding WT condition monitoring systems design and architecture, it is necessary to point out that a number of such systems is available on the market, and that these systems are currently operating worldwide. Main manufacturers of WT condition monitoring systems (followed by system name in parentheses) include Gram & Juhl (M-System MkII), Bachmann (Ω-Guard), Acoem (OneproDMVX), General Electric Bently Nevada (ADAPT.wind), MitaTeknik (WP4086), Brüel & Kjær Vibro (Compass6000/XMS), SKF (IMx T), IFM Electronic (Efector Octavis), SeaCom.

(17) Adam Jabłoński - Methods of Autmatized Monitoring and Diagnosis of Wind Turbines. 17/241 _____________________________________________________________________ Vulcan (MSD Wind), Commtest (Ascent), Prüftechnik (Vibroweb XP), and EC Systems (VIBex). Generally, these systems are based on processing of digitized vibration signals followed by graphical presentation of signal processing results with possible alerting actions. Some continuous systems offer oil debris analysis as well. Within existing systems, vibration signals are collected with sampling frequency exceeding 100 kHz, and with time lengths limited by computer unit storage capacity exclusively. The available plots range from basic time waveforms and spectra to trends, waterfall plots, orbit plots, order spectra, cepstrum plots, constant-percentage bandwidth (CPB) plots, and Bode plots. In case of alerting actions, currently developed systems enable screen messages, SMS messages and e-mail info. WT condition monitoring system configuration refers to selection of parameters of algorithms, definition of characteristic components, definition of machine operational states and configuration of threshold levels. For each of the abovementioned topics, commercial solutions are available. Nevertheless, in author’s belief, currently available WT condition monitoring systems share a number of strong assumptions, which significantly limit their effectiveness. Main assumptions include: - high level of signals stationarity (many systems were simply transferred from other machinery), - steady operational conditions during data acquisition (primitive definitions of machine operational states), - validness of collected data (absence of data validation tools), - data normal distribution (used for threshold settings), - high SNR of characteristic components (application of basic signal processing operations), - local, single faults (lack of tools for distributed or multi-faults). As it will be shown, these and other common pitfalls of WT condition monitoring systems have led the author to undertake a research presented in the thesis.. Regarding the thesis topic, a special attention needs to be given to existing solutions concerning automatized systems and techniques for applicable for WT condition monitoring. Actually, majority of works on automatic diagnostics refer to REB diagnostics [77], [78], and [200]. In his works, the author takes advantage of the assumption of generation of distinguishable signal components upon individual REB fault (i.e. outer race fault, inner race fault, cage fault and ball fault)..

(18) Adam Jabłoński - Methods of Autmatized Monitoring and Diagnosis of Wind Turbines. 18/241 _____________________________________________________________________. 1.1.4. System performance and maintenance Wind turbines are generally aggregated in wind farms far from inhabited areas, like for instance all offshore installations. As a consequence, any physical system maintenance action (not considering machine repairs) requiring on-site expedition is usually of high cost itself. Moreover, due to safety reasons, during any activities inside WT nacelle, the installation needs to be forced to stop and powered off, which generates additional unscheduled loss of profit. Therefore, it is highly advisable to provide a CMS monitoring tools together with the CMS. Most important actions of such system need to perform verification actions concerning: - process channels readings, - vibration channels readings, - oil debris channels readings, - analyses calculations, - states configuration, - thresholds and limits configuration, - channel range settings, - data saving, - network connections. As it will be shown in the thesis, selected maintenance actions might be automatized, significantly rising the level of CMS reliability.. 1.2. Fields of automatization of wind turbine condition monitoring Industrial experience of the author implies that distributed systems of monitoring and diagnosis frequently rely on a large number of manual system configurations due to a relatively high level of unique characteristics of particular machines. Consequently, some details of system solutions are comprehensible to individual system engineers explicitly. Since modern systems of monitoring are a high cost, complex software products, the existence of manual solutions is a major detriment to these systems’ performance. Therefore, implementation of automatization of selected features of the system significantly increases its overall reliability as well as feature extensions. Figure 3 illustrates selected aspects of automatization of distributed wind farms.

(19) Adam Jabłoński - Methods of Autmatized Monitoring and Diagnosis of Wind Turbines. 19/241 _____________________________________________________________________ condition monitoring, including combination of stall-controlled turbines and pitchcontrolled wind turbines.. Figure 3. Proposed general fields of automatization within WT distributed condition monitoring systems. The first field of automatization refers to data acquisition and system configution. Details of data acquisition process tackled in the thesis include configuration of machine operational states, methods for selection of most stable data [118], and a novel topic of automatic validation of machine vibration and process signals [118]. In the proposed scheme, automatic system configuration refers to adaptive channels range settings as well as threshold configuration based on statistical modelling [108].. Automatized data preprocessing proposed by the author refers to the problem of signal resampling and signal separation. Signal resampling is a postprocessing tool minimizing the influence of fluctuating machine speed causing smearing of spectral components. As illustrated by the author in [113], although signal resampling1 has been described by numerous reserchers [143],[208],[207], and[93], application of resampling process to signals from wind turbines might be improved by automatic selection of certain parameters. In [31], the author shows how this novel resampling approach might be used for advanced monitoring of WT planetary gearboxes. Data preproessing includes also a possibility of automatic separation of vibration signals. In contrast to existing latest methods utilizing prediction filters [49] and cepstrum analysis [171], the author aimed to develop a signal separation method characterized by minimized number of parameters, acceptable performance with comparison to. 1. Also known as „order tracking”.

(20) Adam Jabłoński - Methods of Autmatized Monitoring and Diagnosis of Wind Turbines. 20/241 _____________________________________________________________________ other methods, but with low computational burden enabling implementation in commerical continuous WT condition monitoring systems.. Last area of automatization methods concerns data analysis followed by presentation of their results. Within his work, the author has developed an automatic method for selection of optimal frequency band for signal amplitude demodulation, called a “Protrugram”. This method designed for detection and identification of REB malfunctions, described in [24], has been recognized in scientific literature worldwide. The second field of automatization within data analysis refers to creation of a map of vibration signal energy generated by low-speed planetary gearboxes, which was filed for patent [31]. The method takes a direct advantage of abovemetioned autmatized signal resampling from the data preprocessing stage. Last field of automatization proposed by the author refers to generation of automatic diagnostic reports, which aim to imitate a behaviour of a diagnostic engineer in terms of diagnostic investigation. As illustrated in Section 5.4, automatic generation of reports not only might save tremendous amount of time (which transfers directly to cost savings), but it might protect from faults overlooking caused by daily routine as well.. Since WT operation is associated with unpredictable wind force, instead of creating simulated data, major portion of research activities within the thesis was conducted on real WT data, including process parameters data and vibration signals. Because data from WT condition monitoring systems is generally stored in large databases, the author has developed additional tools for data transfer from commercial databases. Based on Matlab® external interface functionality, the author was able to integrate this academic programming environment with external databases. In this way, the author had had the access to vast types and number of WT data enabling development of tools for realization of three abovementioned fields of automatization, i.e. development of algorithms for data acquisition, calcualtion of parameters for system configuration, develoment of signal preprocessing and analysis methods, and finally development of novel techniques for presentation of analyses’ results..

(21) Adam Jabłoński - Methods of Autmatized Monitoring and Diagnosis of Wind Turbines. 21/241 _____________________________________________________________________. 1.3. Justification of the work The reports prepared by The European Wind Energy Association [217] state that at the end of 2010 in European Union a total number of 1132 offshore and 70488 onshore wind turbines was installed, generating 2,95 GW and 84,32 GW of electrical power, respectively. From this numbers it is concluded that nearly 3,5% of total EU wind power comes from the offshore farms. The dynamics of the growth of these markets is presented in Figure 4. The offshore market is relatively new and undeveloped. The first 11 wind turbines mounted on the sea were installed on the Dutch coast in 1991. All together, they generated almost 5 MW of electrical power. For several years, the growth of offshore wind turbine capacity was symbolic – only a few megawatts per year. The jump on the market might be observed in 2001, and from that time almost every year EU utilizes more and more power from the wind on the seashore.. Figure 4. Cumulative, global No. of wind turbines onshore and offshore in the European Union (EU), 1991-2010. This tendency illustrated in Figure 4 is of a special importance for the entire condition monitoring branch because it calls for higher level of reliability of WT condition monitoring systems due to higher maintenance costs comparing to onshore.

(22) Adam Jabłoński - Methods of Autmatized Monitoring and Diagnosis of Wind Turbines. 22/241 _____________________________________________________________________ installations. As it is presented throughout the thesis, the required CMS improvement might be partially realized by implementation of automatized monitoring methods.. The current work undertakes the problem of wind turbine condition monitoring for three major reasons. Firstly, curently wind power is a fastest developing power sector in the world. Until the end of 2011, the total global installed wind capacity reached 239 GW, with an output accounting for 3 % of the total generation capacity in the world. A more impressive data is the newly impressive power, where, as illustrated in Figure 5, the prospects for the future in terms of total wind power capacity are even more in favour.. Figure 5. Total installed wind capacity 1997-2010 [MW], development, and prognostics [216] Secondly, as claimed in [95] and endorsed by author’s industrial experience, available solutions within WT condition monitoring systems do not meet WTs owners’ needs. Tertiary, WT condition monitoring is a multidisciplinary task with emphasis on signal processing, therefore constituting a solid field of academic research.. 1.4. Goal and scope of the work The goal of the work presented in the thesis might be formulated as research towards improvement of reliability and overall efectiveness of distributed WT condition monitoring systems. Within the thesis, it is claimed that it is possible to improve the.

(23) Adam Jabłoński - Methods of Autmatized Monitoring and Diagnosis of Wind Turbines. 23/241 _____________________________________________________________________ reliability of wind turbines by development of improved, automatized signal processing methods. Additionally, the automatiztion might significantly decrease the workload for the vibration experts. The realization of the abovementioned thesis statement is done via 2 general steps: 1. Detection and classification of CMS’ vulnerable elements, 2. Development of methods and tools for WTs’ automatized monitoring and diagnostics.. Within realization of these two steps, the author considers WT as specific types of objects in terms of distributed condition monitoring. This individuality is caused mainly by two factors, namely WT intrinsic characteristics and the large number of machinery consolidated in so-called wind farms. The first (undesirable) intrinsic feature is illustrated in Figure 6 as power and wind fluctuations at nominal speed collected form a stall-controlled WT.. Figure 6. Exemplary stall-controlled WT power and wind fluctuations at nominal speed As clearly observed in Figure 6, some WT experience relatively large power fluctuations even at nearly constant, nominal speed. As shown later in the thesis, other type of WT experiences opposite situation. These fluctuations ruin many signal processing analyses because they change the contents of vibration signal during acquisition [37]. In the thesis, the author proposes to overcome this obstacle by considering three techniques: - additional data acquisition constraints, - continuous verification of acquired data,.

(24) Adam Jabłoński - Methods of Autmatized Monitoring and Diagnosis of Wind Turbines. 24/241 _____________________________________________________________________ - selection of samples corresponding to least non-stationary operational parameters. The second intrinsic WT feature is the low characteristic frequency of selected WT drive train components, especially related to planetary gearbox elements. In author’s proposition, this fact requires customized acquisition of relatively long records, i.e. reaching few minutes. As illustrated in [31], analysis of such signals enables generation of advanced tools for early fault detection.. The second factor concerning sources of problems in WT condition monitoring refers to the large number of machinery under supervision. In a classical approach, CMS are usually duplicated from other machinery to wind farms crating highly error-prone, non-effective solutions. In the thesis, the author proposes to overcome this pitfall by additional consideration of following aspects: - machine individuality, - temporary data storage, - need for continuous system maintenance, - need for automatic data analysis, - data analysis presentation on various levels.. Machine individuality needs to be taken into account due to local wind characteristics. In a classical approach, WT were treated as mutual references like in a machine group monitoring techniques. From author’s research it is concluded that WT manifest a large intrinsic deviation of generated vibration signals within the same WT model. The problem of temporar data storage and need for system maintenance shows how important it is to assure the validity of permanently stored diagnostic estimators calculated from a temporary data. The last considerations, concerning automatization of the process of monitoring concern architectural solutions for data flow and display. This consideration is originated from the fact that WT condition monitoring systems are ought to be realized as multi-layer designs, i.e. they should provide overall, straightforward information about detection of faults as well as sophisticated prediction-oriented information. In the thesis, the author shows how this requirement might be accomplished by integration of external CMS database with scientific programming environment..

(25) Adam Jabłoński - Methods of Autmatized Monitoring and Diagnosis of Wind Turbines. 25/241 _____________________________________________________________________ Within the thesis, the author concentrates on 3 areas of scientific research, namely architecture of distributed CM systems, monitoring and diagnostic of WT drive train elements, and novel digital signal processong (DSP) techniques.. The thesis is structured as follows: Chapter 2 presents a description of wind turbines in terms of objects of vibrationbased condition monitoring, with the emphasis on most commonly encountered drive train designs and associated typical malfunctions and faults. Additionally, wind turbines’ characteristics concerning operating parameters are given.. Chapter 3 constitutes a description of preliminary requirements and constraints of a modern distributed system of wind turbines monitoring and diagnosis. The chapter illustrates author’s innovative automatized solutions concerning criteria for data acquisition from wind turbines operating at variable speed. Within the chapter, the notions of “correct” vibration signal and “valid” vibration signal are introduced. In author’s belief, these novel classifications might find a solid place in general signal classification. Chapter 4 illustrates author’s achievements concerning automatic extraction of signals’ characteristic features. The extraction is conducted via signal decomposition and automatic selection of frequency band for signal demodulation. Chapter 5 presents a new tool in diagnostics of wind turbines’ monitoring, namely automatically generated diagnostic reports, which aim in replacement of a series of actions performed by a diagnostic engineer in order to provide clear, ultimate diagnostic information. Such method enables a decrease of time necessary within a decision-making process. Chapter 6 illustrates the concept of a so-called “Diagnostic Center” (DC), which is a general idea referred to a modern, integrated set of software modules of data acquisition, data transfer, data storage, data analysis, and data access techniques. The first part of the chapter illustrates main tasks of the DC, its architecture, and incorporated modules. Next, the general problem of data access within condition monitoring systems is described. In the second part, the integration of DC with.

(26) Adam Jabłoński - Methods of Autmatized Monitoring and Diagnosis of Wind Turbines. 26/241 _____________________________________________________________________ Matlab® scientific environment is illustrated. Within the chapter, thorough descriptions of Structured Query Language (SQL) database, web service methods, and Matlab data conversion are given. Finally, a data flow diagram illustrating consecutive steps enabling modern, flexible, custom data analysis is presented.. Throughout the thesis, consecutive chapters are composed (in major portion) of author’s own contributions to following scientific publications: [31], [114], [24], [30], [25], [26], [111], [27], [110], [112], [113], [28], [29], [117], [53], [115], [108], [118], and [109]. Majority of listed references have been published in peer-reviewed journals, including Elsevier™’s Mechanical Systems and Signal Processing. and. Measurement: Journal of the International Measurement Confederation from the Thomson Reuters Journal Citation Report List® (JCR).. The author gratefully ackgnowledges Seacom Vulkan GmbH, Herne, Germany, Alstom, Baden, Switzerland as well as Famur S.A., Katowice, Polad for data supply and consultations..


(28) Adam Jabłoński - Methods of Autmatized Monitoring and Diagnosis of Wind Turbines. 28/241 _____________________________________________________________________. 2. WIND TURBINES AS OBJECTS OF VIBRATION-BASED CONDITION MONITORING 2.1. Typical constructions of wind turbines In the thesis, the author focuses on the processing of vibration signals and process signals form wind turbines while trying to take advantage of available information on WT kinematics as much as possible. At the same time, it is necessary to mention that that autor did not consider the analysis of physical phenomena, which take pace during mating of machine parts, like for instance analysis of gear meshing dynamics. Nevertheless, Chapter 2 presents a brief overview of the construction of most typical WTs followed by WT kinematics data. Such data serves a fundamental role for calculation of so-called characteristic frequencies associated with particular machine elements. Although wind turbines might be classified as “Vertical-axis turbines” and “Horizontal-axis turbines“ based by the axis in which the turbine rotates, it has been commonly recognized thet the latter design is nowadays dominating. The most important section of wind turbine construction, in terms of fault detection monitoring, is mechanical drive train. This term encompasses all rotating parts, from the rotor hub to the electrical generator. Most of these components are standardized mechanical parts, which can be taken from existing series production runs. It leads to the conclusion, that classical methods of machines diagnostics can be applied. For correct selection of diagnostics methods, popular concepts of drive train are taken under consideration in Figure 7..

(29) Adam Jabłoński - Methods of Autmatized Monitoring and Diagnosis of Wind Turbines. 29/241 _____________________________________________________________________. Figure 7. Several possibilities of drive train configurations in a wind turbine [96] The figure above shows basic concepts of drive train configuration. Although these solutions have some indisputable advantages, they did not succeed in commercial wind turbines. Only two cases of those mentioned above are being implemented in mass produced turbines. These are: . Whole drive train inline in the nacelle,. . Directly-driven generator.. WT with direct-driven generator are gaining more and more popularity. That is because of some far-reaching advantages. Their construction is simple and compact witch makes maintenance much easier. They can operate with low rotational speed, so rotating parts of drive train have higher durability. Because of this, also energy production downtime is significantly reduced..

(30) Adam Jabłoński - Methods of Autmatized Monitoring and Diagnosis of Wind Turbines. 30/241 _____________________________________________________________________ When geared turbines operational speed range width is 5 to 15 RPM, for direct-driven ones, it can reach up to 25 RPM. Despite number of advantages, direct-driven turbines may not be the “ultimate solution” for wind energy industry, yet. The main trend is to build generators bigger and more powerful, mostly for offshore wind farms. Unfortunately, synchronous generators used in gearless turbines can only reach certain sizes. The cooling of generator is also a problem. That is why direct drive turbines are being mass produced mostly middle sizes (with exception of Enercon). Yet, manufacturers are still developing new technologies, so in the near future gearless wind turbines will have significant influence on the wind energy market.. 2.2. Power control of wind turbines WT are designed to produce electrical energy as cheaply as possible; therefore, they are generally designed so that they yield maximum output at wind speeds around 15 meters per second. (30 knots or 33 mph). Its does not pay off to design turbines that maximize their output at stronger winds, because such strong winds are rare. In case of stronger winds, it is necessary to waste part of the excess energy of the wind in order to avoid damaging the wind turbine. All wind turbines are therefore designed with some sort of power control. Two different approaches are being used mostly: . Pitch control. . Stall control. In pitch-controlled turbines, an anemometer mounted atop the nacelle constantly checks the wind speed and sends signals to a pitch actuator, adjusting the angle of the blades to capture the energy from the wind most efficiently. On a stall-regulated wind turbine, the blades are locked in place and do not adjust during operation. Instead, the blades are designed and shaped to increasingly “stall” the blade’s angle of attack with the wind to both maximize power output and protect the turbine from excessive wind speeds..

(31) Adam Jabłoński - Methods of Autmatized Monitoring and Diagnosis of Wind Turbines. 31/241 _____________________________________________________________________. Figure 8. Pitch-to-stall regulated turbines ratio [5] Until the advent of MW-scale wind turbines in the mid-1990s, stall regulation predominated, but pitch regulation is now the favored option for the largest machines, as illustrated in Figure 8. Currently, about four times as many pitch-regulated turbine designs are available on the market than stall-regulated versions. The prevalence of pitch regulation is due to a combination of factors. Overall costs are quite similar for each design type, but pitch regulation offers potentially better output power quality (this has been perhaps the most significant factor on the German market), and pitch regulation with independent operation of each pitch actuator allows the rotor to be regarded as two independent braking systems for certification purposes.. 2.3. Overview of mechanical drive train elements The drive train elements and the nacelle of a typical large wind turbine are shown in Figure 9. Particular drive train components are described in Table 2..

(32) Adam Jabłoński - Methods of Autmatized Monitoring and Diagnosis of Wind Turbines. 32/241 _____________________________________________________________________. Figure 9. Mechanical drive train and nacelle of N90 wind turbine, 2.3MW, NORDEX [220]. Most modern wind turbines are three-bladed designs with the rotor position maintained upwind. This design is called the classical Danish concept, and tends to be a standard against which other concepts are evaluated [221].. Table 2. Elements of an exemplary wind turbine 1. The Rotor Blades. 11. The Fan Coolers. 2. The Hub. 12. The Wind Measuring System. 3. The Turbine Frame. 13. The Control System. 4. The Rotor Bearing. 14. The Hydraulic. 5. The Rotor Shaft. 15. The Yaw Drive. 6. The Gearbox - 2-stage planetary gear. 16. The Yaw. 7. The Disk Brake. 17. The Nacelle Cover. 8. The Generator. 18. The Tower. 9. The Generator. 19. The Pitch system. 10. The Cooling Radiator.

(33) Adam Jabłoński - Methods of Autmatized Monitoring and Diagnosis of Wind Turbines. 33/241 _____________________________________________________________________ The concept of rotor bearing has significant influence on design of the drive train and the nacelle. In modern turbines constructions, it is frequently demanded to design the transition patch as short as possible in order to achieve greater durability. This solution is incompatible with easy access and maintainability requirements [96]. Consequently, it is of utmost importance to provide hard-wearing drive train components, as well as reliable monitoring system. The traditional solution of rotor shaft and bearing assembly is a shaft on a bedplate with two separate bearings.. In wind turbines, gearboxes are used to multiply rotor rotational speed in order to provide close-to-nominal generator working conditions. Two the most popular gearbox construction used in wind turbines include spur/helical gears and planetary gears. In most common designs, more than one gear stage is required. Small wind turbines are equipped with parallel-shaft gear systems; however, larger constructions are often equipped with planetary gearboxes. Popular solutions, in modern, large, mass produced wind turbines are: two stage parallel-shaft gear plus one planetary and three stages planetary.. Figure 10. Planetary gearbox with two stage parallel gears typical for the 2-3 MW wind turbine power class [96].

(34) Adam Jabłoński - Methods of Autmatized Monitoring and Diagnosis of Wind Turbines. 34/241 _____________________________________________________________________. Along with bearings, gearboxes are common source of drive train faults [96]. The cause of this fact is highly variable load spectrum. During years of exploitation, experiencing great number of faults, designers empirically arrived with optimum gears dimensions, minimizing rate of gearboxes’ faults [96]. However, as illustrated in 2.5, even with modern designs, gearboxes still cause fatal turbine breakdowns.. In wind turbines with electric generators connected directly to the grid, variable loads on the mechanical drive train constitute significant complications. The main requirement refers to the synchronization with the grid.. Figure 11. A so-called “zero-max” WT coupling [218]. Currently, two main techniques to handle the variable-speed transmission in the mechanical drive train are applied, namely doubly-fed generators and synchronous generators with inverter. Moreover, it is worth mentioning that due to mechanical overloads, gearbox shaft is connected with generator via flexible coupling, as illustrated in Figure 11.. 2.4. Wind turbine characteristic frequencies Within his research, the author has investigated WT with a high transmission ratio in contrast to directly-driven WT. Figure 12 presents a typical layout of a wind turbine equipped with a planetary gearbox and parallel gearboxes. The main rotor with three blades is supported by the main bearing and transmits the torque to the planetary gear. The planetary gear has three planets. The planets transmit the torque to the sun gear..

(35) Adam Jabłoński - Methods of Autmatized Monitoring and Diagnosis of Wind Turbines. 35/241 _____________________________________________________________________ The sun shaft is the output of the planetary gear which drives a two-stage parallel gear. The parallel gear has three shafts: the slow shaft connected to the sun shaft, the intermediate shaft and the high speed shaft, which drives the generator. The generator produces AC current of slightly varying frequency. This current is converted first into DC power and then into AC power of frequency equal to the grid frequency. Electric transformations are performed by the controller at the base of the tower. The gearbox setup changes the rotational speed from about 25 rpm on the main rotor to about 1500 rpm at the generator.. Figure 12. A typical layout of a WT with a planetary gearbox A popular example of such solution is the GE – Wind 1.5s wind turbine illustrated in Figure 13, which is a WT model most commonly investigated by the author.. Figure 13. Wind turbine: Manufacturer: GE-Wind, Type: 1.5s, Power: 1500kW, Tower height: 80m, Power control: pitch, Gearboxes: planetary and helical.

(36) Adam Jabłoński - Methods of Autmatized Monitoring and Diagnosis of Wind Turbines. 36/241 _____________________________________________________________________ Figure 14 presents a standard location of sensors on the wind turbine with a planetary gearbox.. Figure 14. Standard location of sensors on a WT with a planetary gearbox In general, the number of sensors depends on the design of the wind turbine. Several setups exist, but the most popular one includes 8 vibration sensors (see Figure 14). Sensors G1 and G2 are used to monitor structural vibrations of the nacelle and the tower. Sensors A1 to A6 measure vibration of the drive train. On some installations, it is possible to combine G1 with A1 and G2 with A2 and only 6 sensors are sufficient for the monitoring. One of those sensors (typically G2/A2) monitors the transversal direction. A1/G1 measures axial vibration and all the others - vertical. Typically, all used sensors are accelerometers, in most cases with ICP® output.. Tables 3-5 show sexemplary chacracteristic frequencies of a WT with planetary gearbox together with exemplary WT bearings’ types. All frequencies are illustrated in a so-called order domain, i.e. relatively to the reference speed signal. In order to provide largest accurace, the reference signal is recorded on the fastest WT shaft, which is the generator shaft..

(37) Adam Jabłoński - Methods of Autmatized Monitoring and Diagnosis of Wind Turbines. 37/241 _____________________________________________________________________ Table 3. Chacracteristic frequencies of a WT with planetary gearbox – Parallel Gearbox Bigger wheel Smaller wheel GMF Bigger wheel relative frequency Smaller wheel relative frequency. Stage 2 91 25 25,0000 0,2747 1,0000. Stage 1 90 22 6,0440 0,0672 0,2747. Table 3 shows two sets of number of teeth (91:25 and 90:22) for the second and the first parallel gear stages. On the basis of this information and with the aid of basic mechanical formulas, consecutive characteristic frequencies are calculated. For parallel gears, the gear meshing frequency (GMF) is calculated by multiplication of the shaft’s speed by the number of teeth on the gear.. Table 4. Chacracteristic frequencies of a WT– Planetary Gearbox. It is worth mentioning that in case of wind turbines, the direction of calculation of characteristic frequencies is opposite to the diresction of power transmission. The power is transmitted from blades through gearboxes to generator, while the calculation of characteristic frequencies starts at the fastest, i.e. generator’s side. That’s why, for instance, “Stage 2” appears on the left side of “Stage 1” in Table 3..

(38) Adam Jabłoński - Methods of Autmatized Monitoring and Diagnosis of Wind Turbines. 38/241 _____________________________________________________________________ Table 5. Chacracteristic frequencies of a WT with planetary gearbox – Bearings. Table 5 implies that a number of different characteristic frequencies are taken into account while analyzing WT data. This is caused by two reasons. Firstly, it often happens that the owner is not “100 % sure” which bearing is being currently mounted. Secondly, due to the design of WT, bearings of different types are physically located near each other on the very same shaft. This latter fact causes a major detriment to overall CM of WTs, i.e. limitation of diagnostic identification due to overlapping spectral bands of various WT elements.. 2.5. Typical malfunctions and faults Despite the fact that wind turbines are usually designed with high safety coefficients for at least 20 years estimated service life, they are relatively prone to unplanned breakdowns and downtime caused by failures. From operator point-of-view, fault causes can be divided to a number of sources, as illustrated in Figure 15..

(39) Adam Jabłoński - Methods of Autmatized Monitoring and Diagnosis of Wind Turbines. 39/241 _____________________________________________________________________. Figure 15. Frequency percentages of the fault causes of WT [130]. The effect of components faults quite often might be unfavorable for the investors. The percentage-wise comparison of kinds of faults that might be differentiated in a wind turbine is shown in Figure 16.. Figure 16. Frequency percentages of the fault effect [131].

(40) Adam Jabłoński - Methods of Autmatized Monitoring and Diagnosis of Wind Turbines. 40/241 _____________________________________________________________________ As illustrated in Figure 16, in majority of cases, faults frequently lead to unplanned downtimes. Moreover, the most of the fault causes are connected with the mechanical components of the discussed objects. Therefore, in order to plan the operational time for a turbine, it is crucial to recognize most popular mechanical failures that might occur during WT exploitation. When discussing basic wind turbines failures, it is advised to point out the elements that are being damaged the most often. Statistical data shows the distribution of turbines failures from 2000 until 2004 is illustrated in Figure 17.. Figure 17. Distribution of number of failures for wind turbines [7] Taking into account Figure 15, Figure 16, and Figure 17 it is concluded that during WT exploitation, efficient and reliable condition monitoring and diagnostics system are of utmost importance. The main general cause of mechanical defect propagation is usually variable load typical for WT. This phenomenon causes acceleration of the aging processes. Additionally, offshore installations as well as those placed on the seashore are exposed to hardly saline environment.. Due to variable loads and rotational speed of the rotor, WT require special treatment towards detection of rolling bearing faults. Additionally, low speed on the first levels.

(41) Adam Jabłoński - Methods of Autmatized Monitoring and Diagnosis of Wind Turbines. 41/241 _____________________________________________________________________ of drive train requires application of more advanced methods of analysis. Nevertheless, a potential degradation of further elements of kinematic chain compels manufacturers of CMS to derive reliable REB diagnostic methods. Another obstacle to WT monitoring emerges from characteristics of planetary bearings, signals from which include multi-modulated components.. The element of drive train, failure of which generates biggest financial loses is the gearbox. Average time of its replacement is about 256 hours [173]. Figure 18 shows the numbers of gearbox failures from 1997 until 2004 in Sweden with respect to the manufacturer and turbine nominal power [173].. Figure 18. Number of gearbox failures from 1997 utill 2004. The most common gearwheels defects include flaking, pitting, and tooth fillet crack. The presence of first two defects usually leads to slow propagation of defects on cooperating wheels, which in long-term can result in accelerated usage of their surface. On the other hand, tooth fillet crack frequently has catastrophic consequences. With.

(42) Adam Jabłoński - Methods of Autmatized Monitoring and Diagnosis of Wind Turbines. 42/241 _____________________________________________________________________ this kind of fault, the cracked tooth might seriously damage other teeth leading to complete destruction of a gearbox.. Last fault typically found within WT drive train is shaft misalignment, frequently caused by coupling defect or assembly errors. Since a WT operating with that kind of fault it is a subject to additional cyclical loads, it undergoes accelerated aging as well..


(44) Adam Jabłoński - Methods of Autmatized Monitoring and Diagnosis of Wind Turbines. 44/241 _____________________________________________________________________. 3. DATA PREPROCESSING 3.1. Elements of data acquisition in condition monitoring systems In spite of modern software and hardware technology, an effective implementation of system of monitoring and diagnosis (SM&D) to large fleets of heavy wind turbines is still a painstaking task for diagnostic engineers [189]. A major difficulty concerning such systems is a constant struggle for reliable system’s functionality due to unpleasant working conditions followed by unpredictable system malfunctions. Just to name a few, condition monitoring for heavy duty systems are faced with large electrical disturbances, sudden load changes, sensor’s saturation, unsupervised cables’ disconnection, and finally costly maintenance access. Apart form abovementioned data error situations, heavy duty machinery is intrinsically characterized by a highly non-stationary operation, which deteriorates a major portion of commercially applied diagnostic indicators from the very beginning. Such condition monitoring circumstances are characteristic especially to wind turbines. Furthermore, as modern monitoring systems are designed for historical data storage, validation of signals to be stored became even more important upon terabytes of disk space frequently wasted for meaningless, corrupted data. As stated in [222], “vibration monitoring relies on accurate and reliable sensor readings”. Consequently, as noticed in [3], “where the sensors are not significantly more reliable than the systems being monitored, the indication of an abnormal state may be the result of a sensor failure rather than a system failure.” In the current chapter, the author presents a comprehensive study on data validation as a prerequisite for data storage followed by data analysis. Based on scientific approach, the author presents a path of data validation, which might be implemented by researches as well as by diagnostic engineers.. Last research shows that one of major reasons for the lack of capability of fault detection were standard (so-called “fixed”) data storage procedures. Basically, such.

(45) Adam Jabłoński - Methods of Autmatized Monitoring and Diagnosis of Wind Turbines. 45/241 _____________________________________________________________________ procedures cause vibration data to be acquired in predefined, fixed time intervals, for instance one sample every 10 minutes. Such policy caused an extremely large number of stored samples to correspond either to highly fluctuating operational conditions or to data experiencing for instance electrical disturbances. In order to overcome these obstacles, author has redesigned the data storage procedure, as illustrated in Figure 19. The figure illustrates two realizations of a data acquisition block illustrated in Figure 120 in Subsection 6.1.2.. Figure 19. Comparison of classical and proposed data storage procedure. In a proposed procedure, fixed data storage is replaced by three consecutive steps, namely validation of process parameters, selection of vibration data, and finally validation of vibration data. Consecutive Substeps of a proposed procedure are described in following Sections in details.. 3.2. Criteria for correct data acquisition 3.2.1. Process parameters validation Validation of process parameters refers to (preferably continuous) reading of available machine process parameters (most commonly speed and power or load) and conclusion about their likelihood. This step needs to be achieved in two courses, independent and relative..

(46) Adam Jabłoński - Methods of Autmatized Monitoring and Diagnosis of Wind Turbines. 46/241 _____________________________________________________________________. 3.2.1.1. Independent process parameters validation Firstly, predefined maximum and minimum thresholds might be set on process parameters’ readings. For instance, given a wind turbine operating with speed up to 1500 rpm, these absolute limits might be set at zero and 2000 rpm as lower and upper boundary, respectively. Such limits need to be assigned to all process parameters regardless of machine behavior and other sensors’ readings.. Secondly, certain process parameters, like load in case of underground conveyors or wind speed, might be required to change its value within certain time, since it might be assumed that theses physical quantities must vary.. Tertiary, it might be beneficial to implement constrains of maximum allowable instantaneous change of process values, i.e. to prohibit sudden changes. Just as, in reality, within a fraction of a second, wind will not suddenly stop from 25 m/s. One powerful technique to achieve such constrains is to set a threshold on a maximum value of first-order difference of process time series, which in case of discrete signal x = xi, xi+1,…xN is calculated straightforward as max(|(xi+1,…xN) - (xi,…xN-1)|). As illustrated in Figure 20, this method is preferable over common mean-oriented (typically standard deviation) or intuitive raw peak-to-peak (PP) indicators. Fig. 3.a illustrates a time series of an exemplary process parameter, which experiences data error at 50th sample. In this case, all listed indicators, i.e. mean-oriented methods, PPoriented methods as well as preferable first-order-difference-based method perform alike providing a clear evidence of abnormality. In case of presence of a linear process value increment illustrated in Figure 20.c, PP and standard deviation fail because they indicate significantly larger value, which is inadequate to actual lack of change in signal quality comparing to signal illustrated in Figure 20.a. Moreover, these estimators indicate large values in case of an exemplary error-free signal illustrated in Figure 20.e..

(47) Adam Jabłoński - Methods of Autmatized Monitoring and Diagnosis of Wind Turbines. 47/241 _____________________________________________________________________. 50. Value. 40. b. a 20. 0. 0. -50. Value. 20. 40 20 0 -20 -40. 80. 100. 40 60 Samples. 80. 50 d 0 -50 40 60 Samples. 80. 40 20. 100. 20. 40 60 Samples. 80. 50 f. e. 0 -20 -40. 20. c. 20. Value. 40 60 Samples. 0 -50 20. 40 60 Samples. 80. 100. 20. 40 60 Samples. 80. Figure 20. Exemplary process signals: a) with data error, c) with linear increment and data error, e) with non-linear fluctuations, and their first-order differences (b,d,f), respectively. On the contrary, the proposed maximum value of signal first-order difference is essentially insensitive to signal shape (i.e. distinguishable low-frequency component) because it informs about change from one sample to another exclusively. To some extent, data with a low-frequency component (c and e) could undergo a detrending operation; however, this might become a complicated operation for polynomial components of the order greater than one [39]. Another benefit from implementation of first-order-difference-based approach is a direct physical interpretation of threshold values, e.g. allowable maximum instantaneous change in machine speed equal to 200 simply means that machine must not accelerate or decelerate more than 200 speed units (for instance rpm) in a signal sampling time..