based on process models
Proefschrift
ter verkrijging van de graad van doctor aan de Technische Universiteit Delft,
op gezag van de Rector Magnificus Prof. dr. ir. J.T. Fokkema, voorzitter van het Colleges voor Promoties,
in het openbaar te verdedigen op maandag 21 februari 2005 om 15:30 uur
door
Hugo Trienko GRIMMELIUS
werktuigbouwkundig ingenieur geboren te Dinteloord en Prinsenland.
Prof. ir. H. van der Ree
Samenstelling promotiecommissie:
Rector Magnificus, voorzitter
Prof. ir. J. Klein Woud, Technische Universiteit Delft, promotor Prof. ir. H. van der Ree, Technische Universiteit Delft, promotor Prof. dr. ir. A.H.C. van Paassen, Technische Universiteit Delft
Prof. dr. ir. P.M.J. van den Hof, Technische Universiteit Delft
Dr. E. Mesbahi, University of Newcastle upon Tyne, United Kingdom Prof. ir. D. Stapersma, Koninklijk Instituut voor de Marine en Technische
Universiteit Delft
ing. G. Been, vh. Van Buuren - Van Swaay B.V.
Prof. dr. ir. G. Lodewijks, Technische Universiteit Delft, reserve lid
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Delft University Press P.O. Box 98 2600 MG Delft The Netherlands Telephone: +31 15 27 85 678 Telefax: +31 152785706 E-mail: info@library.tudelft.nl ISBN 90-407-2562-4
Keywords: condition monitoring, fault diagnosis, marine refrigeration, simulation Copyright © 2005 by H.T. Grimmelius
Table of Contents . . . V Preface . . . XI Introduction . . . 1
Condition Monitoring 1
Research history 3
Structure of this thesis 5
1 Enhanced Condition Monitoring . . . 7
1.1 Introduction 7
1.2 Terminology 9
1.3 Fault diagnosis 13
1.3.1 Introduction 13
1.3.2 Developments 14
1.4 Examples: two methods for enhanced condition monitoring 15 1.4.1 Fault diagnosis using regression models and pattern recognition 15
1.4.2 Fault diagnosis using Artificial Neural Networks 22
1.5 Research approach 27
1.5.1 Focus 27
1.5.2 Challenge 28
1.5.3 Hypothesis 28
1.5.4 Limitation 29
2 Modelling for failure mode simulation . . . 31
2.1 Requirements for a failure mode simulation model 31
2.2 General modelling concept 34
2.3 Concept of the refrigeration plant model 36
2.3.1 Choice of in- and output variables 37
2.4 Compressor model 40
2.4.1 Compressor model concept 40
2.4.2 Initial compression cycle model and associated assumptions 42
2.4.3 Adjustments to the compression cycle model 44
2.4.4 Other vessels in the compressor 46
2.4.5 Resistors in the compressor 48
2.4.6 Mechanical and electrical losses 48
2.5 Condenser model 50
2.5.1 Condenser model concept 50
2.5.2 The refrigerant side 51
2.5.3 Heat transfer 53
2.5.4 Water side 55
2.6 Liquid line and expansion valve model 57
2.6.1 Liquid line and expansion valve model concept 57
2.6.2 The valve section: the throttling process 58
2.6.3 Actuator mechanism: the bulb and the motion of the valve 59
2.6.4 Intermediate pressure calculation 60
2.7 Evaporator model 61
2.7.1 Evaporator model concept 61
2.7.2 Refrigerant segment: two-phase flow 64
2.7.3 Refrigerant segment: one-phase flow 66
2.7.4 Tube wall segment 68
2.7.5 Water segment 68
2.7.6 Shell wall segment 70
2.7.7 Implementation and development of the model 70
2.8 Simplified models 73
2.8.1 Simplified compressor model 74
2.8.2 Simplified evaporator model 74
3 Model verification, matching and validation . . . 75
3.1 Introduction 75
3.2 Measurements 78
3.2.1 Measured variables 78
3.2.2 Measuring procedures 78
3.2.3 Data used for matching and validation 79
3.3 Verifications, validation and matching of the component models 81
3.3.1 Verification of the component models 81
3.3.2 Matching and validation of the component models 82
3.5.3 Validation of the condenser model 100 3.6 Liquid line and expansion valve model matching and validation 102 3.6.1 Adjustable parameters in the liquid line and expansion valve model 102 3.6.2 Matching the liquid line and expansion valve model 102 3.6.3 Validation of the liquid line and expansion valve model 105
3.7 Evaporator model matching and validation 107
3.7.1 Adjustable parameters in the evaporator models 107
3.7.2 Matching the evaporator models 107
3.7.3 Validation of the evaporator models 109
3.8 Validation of the plant models 112
3.8.1 Introduction and procedure 112
3.8.2 Validation of the plant model using the detailed compressor model 113 3.8.3 Validation of the plant model using the simplified compressor model 117
3.9 Conclusion 121
4 Failure mode effects: measurement and simulation . . . 123
4.1 Approach 123
4.2 FMEA, possible failure modes 125
4.3 Failure mode - symptom pattern matrices 128
4.3.1 Introduction 128
4.3.2 Improvements and extensions 129
4.4 Measured symptoms 131
4.4.1 Implementation of failure modes 131
4.4.2 Symptom pattern extraction 134
4.5 Simulated symptoms 143
4.5.1 Implementation of failure modes in the models 143
4.5.2 Simulation results 146
4.5.3 Comparison of simulated symptoms with measured symptoms and symptoms found
in previous research 151
4.5.4 Influence of failure modes on dynamic behaviour 159
4.6 Conclusion 162
5 Conclusions and Recommendations . . . 163
5.1 In retrospect 163
5.2 Conclusions 165
5.2.1 Overall 165
5.2.2 Prediction of faulty and off-design behaviour 165
5.2.3 An effective diagnostic strategy 166
5.3 Recommendations for further research 167
5.3.1 Prediction of faulty and off-design behaviour 167
Appendix A Description of test facilities . . . 169
A.1 The laboratory chiller and sensors 169
A.1.1 Plant layout 169
A.1.2 Additions for failure mode implementation 171
A.1.3 Sensors 171
A.2 The reciprocating compressor 174
A.3 The condenser 176
A.4 The liquid line and expansion valve 178
A.4.1 Liquid line 178
A.4.2 Thermostatic Expansion Valve 179
A.5 The evaporator 180
Appendix B Description of measurements . . . 183
B.1 Data conversions 183
B.1.1 Refrigerant mass flow measurement 183
B.1.2 Pressure corrections 185
B.2 First series: September 1994 185
B.2.1 Description 185
B.3 Second series: June 1995 186
B.3.1 Description 186
B.3.2 Failure modes in second series 187
B.4 Third series: September 1995 188
B.4.1 Description 188
B.4.2 Failure modes in third series 189
Appendix C Detailed model descriptions . . . 191
C.1 Compressor model 191
C.1.1 Mass and energy equations for the cylinder 191
C.1.2 Heat exchange in the cylinder 195
C.1.3 Mass flows 196
C.1.4 Piston movement 198
C.1.5 Mass and energy equations for other vessels 199
C.2 Simplified compressor model 200
C.3 Condenser model 202
C.3.1 Vessel equations 202
C.3.2 Solving the algebraic loop in the equations for the condenser 205 C.3.3 Heat transfer coefficients for the condensing zone 206 C.3.4 Heat transfer area for the shell-and-tube condenser 207
Appendix D Implementation of the models . . . 223
D.1 Choice of software 223
D.1.1 Time domain simulation model building 223
D.1.2 Numerical solution 224
D.1.3 Some statistics 225
D.2 Example of model construction 226
D.3 Example of parameter input 230
D.4 Added models to overall plant model 232
D.4.1 Input module 232
D.4.2 Overall energy balance 232
D.4.3 Average output estimator 233
Appendix E Additional simulation results . . . 235
E.1 Comparing operating conditions 235
E.2 Response time to failure mode 240
E.3 Symptom levels: a closer look 245
E.4 Details of compressor cycle related failure modes 250
E.4.1 Suction valve failure modes: leaking and increased resistance 250
E.4.2 Cylinder failure mode: leaking piston rings 251
E.4.3 Discharge valve failure modes: leaking and increased resistance 251 Appendix F Properties of R22 and water . . . 253
F.1 Properties of refrigerant R22 253
F.1.1 General properties 253
F.1.2 Saturated conditions 254
F.1.3 Gaseous (superheated) conditions 259
F.2 Properties of water 262
F.3 Some special subjects 264
F.3.1 Compressibility factor Z for R22 264
Appendix G Nomenclature . . . 267 G.1 Symbols 267 G.1.1 Letters 267 G.1.2 Greek symbols 268 G.2 Subscripts 269 G.3 Prefixes 271 G.4 Non-dimensional Numbers 272
References . . . 273 Indexes . . . 279 Figures 279 Tables 285 Summary . . . 289 Samenvatting . . . 291 Acknowledgements . . . 295 Curriculum Vitae . . . 297
In 1992 the ICMOS (Intelligent Condition MOnitoring Systems) research project was well underway for diesel engines used for propulsion and power generation onboard ships. Trying to extend this research and find other application for the results and experience led to research on condition monitoring for compression refrigeration plant, as these plants are often used and also often not well understood by the marine engineer.
The initial, and necessarily very general, research assignment for this project was:
'To generate knowledge required for the condition monitoring and fault diagnosis of compression refrigeration systems on board ships.'
Now unfortunately, not a ship or even a reference to ships will be found in this thesis beyond the introduction. Furthermore, little is said on fault diagnosis as such, and condition monitoring is taken as a prerequisite. Which leaves 'to generate knowledge of compression refrigeration systems'.
The main concept is to use numerical process simulation models to 'generate' this knowledge necessary for condition monitoring of compression refrigeration plants. One of the initial ideas was to use existing simulation models as a starting point for the research. Unfortunately, it turned out that no models were readily available or suitable for the research of off-design behaviour, such as failures. Much effort has therefore been put in the development and implementation of new models, based on available knowledge and literature. To verify, match and validate these models, measurements were conducted on an adapted and instrumented laboratory chiller. The measurements also included the introduction of failure modes, to validate the results found with the simulation models. However, the old Dutch proverb 'meten is weten' ('to measure is to know') was again contradicted.
As often is the case with research projects, an attempt to answer the initial question leads to a cataract of new questions, each fully acceptable as a S new S research topic. Trying to avoid being pulled off track too much, but still taking enough sidesteps to make sure of the right direction, is a challenge for all research. Also in this research some sidesteps almost turned out to be sidetraps.
Condition Monitoring
Over the last decades the reliability, availability and safety of plants has become increasingly important for a number of reasons. One of the reasons is the increasing size and complexity of the plants, both onshore and offshore. The cost and safety risks have increased with the size, and the complexity has led to extensive automation. The human operator is not able to control the entire plant, nor able to assess its running condition, without the aid of this automation. However, the amount of data available to the operators is enormous. Many parameters, variables and alarms are available simultaneously for evaluation. Hence the need for automated evaluation of the data, or enhanced condition monitoring (sometimes incorrectly referred to as 'intelligent' condition monitoring).
On board ships
On board ships other factors underline the need for enhanced condition monitoring: the changing task of the marine engineer, the reductions in crew size and the rationalisation of maintenance [Carlton, 1996]. The main task of a marine engineer has changed from the task of a maintenance engineer, who also controls all the machinery by hand, to the task of a supervising operator, who only takes decisions and uses the automation to handle the machinery.
Economically required reduction in crew size in recent years has stimulated automation. Crew size and their wages are among the last of the not globalized running costs for ships. Several ship owners have started to employ generally trained officers who serve both on the bridge
and in the engine room. Automation, through the unmanned engine room and the one man watch system on the bridge, made a smaller crew possible, with reduced qualifications.
The reduction of crew size has reduced the possibilities for the crew to perform major maintenance tasks, such as a cylinder overhaul. The tight time-schedules for modern freighters leave little margin; therefore the use of service crews for maintenance has become common praxis, for increasingly more maintenance tasks. This enables again further reduction in crew size and justifies the employment of less qualified personnel.
An important complicating factor for enhanced condition monitoring is the low acceptance of unnecessary or false alarms on board ships. The engineer has many tasks he must attend to during his watch (daily maintenance, small repair work, administration) and often there is no backup personnel available. With generally trained officers this is even more the case since they also have to attend to the ship's navigation and safety at sea. Thus a highly accurate and reliable monitoring system is required [Mensonides cs., 1989].
Marine refrigeration plants
On board ships refrigeration plants are used for domestic uses (food stores, air conditioning), cargo conditioning and to fulfil special cooling requirements (electronics and weapon systems on naval vessels). The plants used for cargo conditioning are complex and sophisticated, while this cargo is often expensive and very susceptible to temperature changes. Furthermore, in recent years, the transport of refrigerated cargo by sea has been steadily increasing [Stera, 1995]. Failures are to be detected promptly and correctly to enable timely corrective action by the crew. Monitoring refrigeration plants has proven to be a difficult task for the ship's crew, since often expert knowledge is required to assess the plants working condition. For these reasons, ship owners requested research into enhanced monitoring systems for marine refrigeration plants.
On intelligent and intelligence
The use of the word 'intelligent' in conjunction with condition monitoring systems is avoided as much as possible in this thesis. Condition monitoring systems do not show intelligence: they do not have 'the ability to gain and apply knowledge and skills' [Oxford, 2002]. Even though, when referring to machines or devices, commonly the word 'intelligent' is already used if it is 'able to vary its state or action in response to varying situations and past experience' [Oxford, 2002], often this 'past experience' is limited to the knowledge put into it at commissioning.
Research history
In light of the developments discussed in the previous section, the research into techniques and tools for the enhanced engine room has become one of the main topics at the Department of Marine Technology of the Delft University of Technology. In 1986 the ICMOS (Intelligent Condition Monitoring Systems) project was started. At the onset, it focussed on an enhanced condition monitoring system for the propulsion diesel engines of naval frigates [Klein Woud cs., 1993; Bergman cs., 1993]. In later years projects were initiated for the engine cooling system [Van Herwerden, 1993] and compression refrigeration plants [Grimmelius cs., 1995].
In 1992 a prototype system was developed to assess the possibilities for, and problems with, the development of an enhanced monitoring system [Grimmelius cs., 1995]. It was developed for a chilled water plant used for air conditioning. Measured values were compared with calculated reference values and a pattern recognition routine was used to match the resulting symptom patterns with the symptom patterns of known failure modes.
This prototype showed that there were several problems that needed a more thorough approach. One problem was posed by the pattern recognition routine used to diagnose the plant once a deviation was detected. The pattern recognition implemented in the prototype showed some serious flaws in handling failure modes which caused only a limited number of symptoms. Also the possibilities to use more complex symptoms, such as time constants and other dynamic parameters, are limited. Thus, there is a need for an effective diagnostic strategy.
Another, more serious, problem turned out to be the prediction of the behaviour of the refrigeration plant in off-design conditions or in faulty conditions, even for the relatively simple chilled water plant that was used as test plant. This is caused by the closed loop character of the refrigeration process and the complexity of the components. The effect of, for instance, an increased resistance will propagate through the refrigerant cycle and all components will operate in a new working point, leading to changes in temperatures and pressures throughout the plant. These changes are very much dependent on the relative sizes of the components and the way they are interconnected and controlled, making predictions difficult. Thus, there is a need for prediction of faulty and off-design behaviour.
In order to address these two areas, in 1993 a new line of research was started, 'To generate knowledge required for the condition monitoring and fault diagnosis of compression refrigeration systems on board ships'.
Prediction of faulty and off-design behaviour
The main focus of this research is on the prediction of off-design system behaviour through the use of simulation models. The goal for this research is to design and implement simulation models suitable to reliably predict qualitative symptom patterns of failure modes, based on manufacturers data of the equipment and only a limited amount of measurements of the healthy plant. The models should be based on first principles and the results should be validated using measured data of faulty behaviour.
To obtain the measurements needed for validation of the faulty behaviour a small laboratory water chiller is available. A simple plant layout is chosen to reduce the amount of parameters and variables to be taken into consideration in cause and effect studies. Some adjustments and additions are made to facilitate the coordinated introduction of failure modes.
The first principle models that are used to predict off-design behaviour, are parametric, modular and implemented in a commercial available simulation tool.
An effective diagnostic strategy
A literature survey has been conducted to obtain an overview of available diagnostic strategies [Grimmelius, 1993].
Consequently, research was conducted into the application of artificial neural networks (ANN) as a diagnostic tool, resulting in a prototype diagnostic tool [Van Kuilenburg, 19952;
Grimmelius cs., 1999]. This research showed the good potential of ANN as part of a diagnostic strategy, but also emphasized the need for reliable prediction of faulty behaviour.
Structure of this thesis
This thesis contains five main chapters. In Chapter 1, the concept of enhanced condition monitoring is introduced. The terminology is defined and the results of the two developed prototypes mentioned in the previous section are shown. In the final section of Chapter 1 the scientific challenge is formulated and some hypotheses are postulated.
Chapter 2 starts with the requirements for the models used in this research. Given these requirements, a general modelling concept is adopted, based on a first principle approach. A short description of the plant and its main components is given, including the input and output variables. The main part of the chapter is the description of the component models.
In Chapter 3 the procedures used for verification, matching and validation of the models are described. An overview of the measured variables is given and the measurements used for the matching and validation of the component models in healthy condition are described. The results of the matching and the healthy behaviour simulations are illustrated, both with steady state and dynamic responses, both for the individual component models and the overall plant model.
In Chapter 4 the effects of failure modes on the plant, both measured and simulated is described. The possibilities and limitations of failure mode - symptom pattern matrixes are discussed. The procedure for the introduction of the failure modes in the plant and the results of the measurements are shown and discussed. From these measurements a measured failure mode - symptom pattern matrix is derived, using the simulation model as reference. The modelling of the failure modes is described, and the results from the faulty behaviour simulation are used to determine a simulated failure mode - symptom pattern matrix. The suitability of the approach chosen in this research is shown through comparison of the two symptom matrices.
Finally Chapter 5 sums up the results and findings. It discusses the main topics of this research, draws overall conclusions, tests the hypotheses against the results, and gives recommendations for further research.
Monitoring
This chapter gives an introduction to enhanced condition monitoringand explores the field. The terminology is defined and illustrated. Two different approaches to Enhanced Condition Monitoring are illustrated with a developed prototype. These examples show that the prediction of faulty behaviour is the main problem for the further development of enhanced condition monitoring systems.
1.1
Introduction
The main objective of a monitoring system is to support the human operator in ensuring safe and economical operation of a plant, within the operational limits. In case of a fault, most systems still require a human operator for instant evaluation of the alarms and messages since normal control routines no longer apply. Because of the complexity of the plants and the time pressure, this can easily lead to wrong conclusions and subsequently to wrong actions. In many serious accidents with complex plants the wrong interpretation of the information provided by the monitoring system has dramatically increased the consequences (Tjernobyl, Three Mile Island, ms. Prinsendam etc.). The use of an enhanced monitoring system with decision support capabilities, based on knowledge concerning the faulty behaviour, could help the operator in taking the right decisions quickly, and thus help to reduce the effects [Hoegee, 1993].
To be able to successfully assist the operator, the designer and builder of an enhanced condition monitoring system must have access to, or have at his/her disposal:
< Knowledge components and processes:
Depending on the required possibilities of the decision support system, demands are set for the necessary level of knowledge and the required amount of sensors and affiliated
equipment. If fault diagnosis on a detailed level is required S for instance leakage of a valve on a specific cylinder S then detailed knowledge is needed of the components of the plant and their respective behaviour, and much information is needed, often implying the use of many sensors. If a fault diagnosis at an early stage of failure development is required S for instance the start of fouling of a heat exchanger S then detailed knowledge is needed of the processes in the plant, and the way the processes influence each other, to determine the initial symptoms caused by the fault. Furthermore, accurate measurements and an accurate prediction of healthy behaviour are necessary. This not necessarily implies the use of many sensors.
< Reference values for monitored variables, in healthy system condition:
In order to be able to detect deviation, knowledge of the healthy values is needed, given the actual ambient and operating conditions. These reference values need to be sufficiently accurate to enable the detection of deviations. Sometimes it may be sufficient to use average values, or to detect rapid changes in certain values. This however limits the accuracy of the reference values, and thus impairs the fault detection, especially in ambient or operating conditions that do not occur often.
It is difficult to predict sensor values from general design information, without actual measurements, because the values are strongly dependent on the specific dimensions and the lay-out of the plant. Another complicating factor is that the plant's behaviour is dependent on operating conditions, and effects caused by failure modes may vary with these conditions. < Descriptions of possible failure modes and their effect on the plant's behaviour:
An important aspect of the decision support system is the correct assessment of the cause of the observed deviations. A causal analysis of the plant may proof sufficient to find the direct consequences of all relevant failure modes, but the translation of these direct consequences S such as a decreased mass flow S into actually measured deviations S such as a decreased temperature S can proof to be very difficult [Grimmelius cs., 1995].
< An effective strategy that enables deviations to be detected promptly and failures to be diagnosed correctly:
From the problems listed above it becomes clear that the decision support task is a complex one. Extra knowledge has to be added, or combinations of techniques have to be used to obtain a reasonable response time.
< Thorough knowledge of the support, required by the human operator, and the format in which this support has to be given:
The man-machine interface has become an essential part of the system because of the amount and complexity of the information that has to be evaluated by the operator. Careful design and planning are required to ensure the proper format (both order and amount) of information
1.2
Terminology
When considering terms like fault, failure and symptom, many connotations appear in literature and general usage. This is also true for many other terms, used in coherence with the scope of this subject, like decision support systems, artificial intelligence, expert systems, knowledge systems, etcetera. In this thesis the following definitions have been chosen:
A plant consists of interconnected components, where a component is a clearly separable part of the equipment, with a distinguishable function in the process, like an evaporator, a compressor, a sensor. Parts are the smallest units in which a component can be split without destructive means: the Least Replaceable Units (LRU), for instance a valve spring, a bearing, a valve actuator.
A failure cause identifies the source of the failure, for instance wrong design, poor maintenance, accident. A further distinction can be made between internal (from the component or part itself), external (from another component or part) or ambient (from outside the influence sphere of designers and operators).
A failure mechanism is the process that leads to a failure mode. For instance fatigue, abrasive particles in the oil, scaling.
A failure mode is a physical phenomenon which results in loss of functionality of a part, component or plant. For instance a broken valve, excessive wear of a bearing, fouling in a heat exchanger.
A failure is the perceptible occurrence of a failure mode. It is therefore an event that causes perceptible deviation of the system from normal operation, for instance the breaking of a valve, the occurrence of a substantial leak, noticeable fouling in a heat exchanger.
A fault is a state of a plant, component or part where there is an unacceptable deviation between one or more measurable quantities and the intended reference conditions of those quantities. A fault is the result of one or more failures, and is the measurable effect of one or more failure modes.
Failure effects describe the resulting impact of the failure, not only on the component or plant, but also on the operation and safety (e.g. of the ship), and on the environment. For instance loss of propulsion, fire hazards, emission of toxic gases.
A deviation is any difference between an intended reference condition and the measured actual condition of a quantity.
A residual is the numerical representation of a deviation. It is the difference between an intended reference value and the measured actual value of a quantity.
Function Failure mode Failure mechanism Failure cause Functional requirements Fault Failure analysis Failure effects Diagnosis Symptom
Figure 1.1 Terminology: from failure cause to
failure effects.
A symptom pattern is a set of symptoms that belong together, either because the symptoms occur (and are measured) at the same time, or because the symptoms represent the effect of a specific failure mode on a plant.
A threshold gives the acceptable size of a residual. Most often there is a lower and an upper threshold related with a quantity. Upper and lower thresholds need not be equal nor do they have to be constant.
(Fault) diagnosis is the determination of the failure mode resulting in the observed symptoms. Depending on available data and knowledge, the failure may be found on component or part level.
A diagnostic strategy describes the procedure that is used to identify the cause of a fault from a given set of symptoms. It can consist of several diagnostic techniques.
A diagnostic technique is a single numerical or logical procedure to extract a diagnosis from presented data.
Failure analysis is the determination of the failure cause. These definitions above are illustrated in
Figure 1.1, adjusted from Smit [1988]. The cause-effect chain from failure cause to failure effect is shown from the bottom left corner to upper left corner. A failure cause will result in the initiation of a failure mechanism that will influence the component through a failure mode. As soon as the failure mode influences the function a failure has occurred, resulting
in measurable deviations: symptoms. Only then a diagnosis is possible, and the first goal will be to determine the failure. Of course, any information on the failure mode, the failure mechanism and ultimately the failure cause could be useful to the user. A fault is a state of the plant or component in which it does not meet the functional requirements anymore. This may cause further problems: the failure effects.
Diagnosis and failure analysis are clearly separate loops. Diagnosis may very well be possible on-line, but failure analysis is often only possible after the failure has occurred and a detailed analysis is possible.
A monitoring system provides measurements to the user at a location, different from the direct location of the measurement.
An enhanced monitoring system adds interpretation of these symptoms, for instance through (fault) diagnosis or trend predictions.
An intelligent monitoring system adds a self learning capability and the faculty of reason, enabling the system to learn and improve while in use.
A reference value generator is part of the condition monitoring system and provides the expected reference values for the measured quantities.
The diagnostic resolution (distinguishing ability, detect ability) is the certainty and precision with which the cause of a fault can be identified. Diagnostic resolution is influenced from two sides. On one side, there is a limitation in the possibilities as a result of limitations in the monitoring system. This results in an impossibility to determine a detailed diagnosis for certain failure modes, due to lack of information. For instance, in a compression refrigeration plant, if no gas detection sensors are installed, small leakage of refrigerant will go unnoticed until the plant shows a lack of capacity. On the other side, different diagnostic strategies also result in different limitations. This results in an impossibility to determine a detailed diagnosis for certain failure modes, due to lack of knowledge. For instance, an enhanced monitoring system based on heuristic knowledge will not be able to identify unforseen failure modes. A decision support system is the entire system of equipment and knowledge needed to support the human operator in making decisions. Hence, it contains for instance a monitoring system, one or more reference value generators, one or more diagnostic strategies and a post-processing system that translates the diagnosis into operational, maintenance and repair advises to the operator.
Knowledge systems are considered to be all systems, designed to store specific and detailed knowledge and use this knowledge to judge new situations.
Expert systems are a limited class of knowledge systems, which are characterized by a strict separation of the knowledge (in a knowledge database) and the inference mechanism.
Knowledge sources are the origins of the knowledge implemented in the knowledge database. For instance Failure Mode and Effect Analysis (FMEA), interviews with experts, measurements, experiments, simulations.
Knowledge mining is the excavation of knowledge from the knowledge sources, and presenting it in a suitable form.
Real-time implies that the plant is monitored continuously and that diagnoses are given often enough to make preventive corrective actions normally possible. Thus, "real-time" differs per system and per fault. For instance, fouling generally grows slowly, and could be monitored daily, while wear of a bearing can suddenly become a source of heavy vibrations and subsequent extensive damage, and should be monitored almost continuously, e.g. every second. It is conceivable that within a complex real-time enhanced monitoring system different time bases for diagnosis are maintained.
A system is considered on-line when collecting and interpreting data occurs simultaneously and real-time.
Validation Health monitoring Plant Healthy? Reference value generator + -Yes Diagnosis Knowledge database Knowledge sources reference values residuals symptoms sensor readings input signals No
Figure 1.2 Terminology: Enhanced Condition Monitoring.
Figure 1.2 gives a general overview of a plant with enhanced condition monitoring. The measured input signals to the plant are also used to generate reference values. These are compared with validated sensor readings from the plant's output, giving the residuals. Symptoms are derived in the health monitoring part. After the decision 'healthy or not', diagnosis is attempted using the knowledge database, which has been filled using different knowledge sources.
Figure 1.3 General principle of fault diagnosis.
1.3
Fault diagnosis
1.3.1 Introduction
Fault diagnosis (FD) as defined in the previous section is a mapping function from the symptom information space S formed by all possible information provided by the health monitoring system S into the possible cause space S formed by all possible failure modes S Figure 1.3. This mapping, however, need not be exact: both the information, the data, and the results, the failure modes, can contain ambiguities and uncertainties that have to be dealt with correctly.
Information from the monitoring system can be diverse, based on residuals that are found using a variety of data. With an increasing level of detail, the necessary mapping function becomes more complex.
In general a plant can be divided into three major subsystems: the actuators, the actual plant and the monitoring system. Often, parts of the measured output are used in a feedback control loop, which emphasises the importance of a reliable and accurate monitoring system. Usually, an enhanced condition monitoring system is designed to detect faults in these three subsystems separately.
Criteria for assessing performance of a diagnostic strategy are [Clark, 1989]: 1) promptness of detection;
2) sensitivity to incipient (i.e. small or slowly developing) faults; 3) false alarm rate;
4) missed fault detections; 5) incorrect fault detections.
Hence, an ideal fault detection system will promptly (1) react to all faults (2, 4) with the correct diagnosis (5), without reporting faults when there are none (3).
An important problem is the strong dependency of the results of a diagnostic strategy on the plant that is being monitored and the condition under which it is operating. A general
conclusion as to what strategy will perform best, is therefore not possible. Even if the individual plant to be monitored is taken into consideration and the operating conditions are known, the number of influence factors is such that a definite conclusion is impossible, only indications as to which strategies are promising, and which are not, can be given. Theoretical robustness and sensitivity can be calculated if ample information about noise influences, nonlinear elements, sensor accuracy and bias, etcetera, is available. This will only be the case for relatively small and simple plants, or for simulated plants.
1.3.2 Developments
The introduction of fault diagnosis to condition monitoring systems is a much researched area. Lately a lot of attention is given to the application of artificial neural networks [Mesbahi, 2002; Yu, 2003]. Yu [2003] also gives a concise overview of the developments in the recent years in the area of heating, ventilation and air conditioning systems (HVAC). Noteworthy is the fact that for HVAC systems major international research programmes have been executed, under the IEA Annex 25 [Peitsman, 1996; ECBS, 20041] and Annex 32
[Dexter cs., 2001; ECBS, 20042], which include fault diagnosis as an important topic.
For marine systems however there is less coherent development. Classification societies have recognized the importance of condition monitoring [Carlton, 1995], specially in relation with condition based maintenance and survey. Developments are however for specific equipment only, with the (propulsion) diesel engine as the main subject [Logan, 2001; Mesbahi, 2000; Klein Woud cs., 1993; Bergman cs., 1993]. The two main manufacturers of diesel engines for ship propulsion now supply fault detection and diagnosis modules with their proprietary monitoring systems: Wärtsilä [2003] provides FAKS, MAN B&W [2004] provides CoCoS-EDS. Rule based reasoning is the predominantly implemented diagnostic technique.
Figure 1.4 Concept for on-line diagnosis. [Grimmelius cs., 1995]
1.4
Examples: two methods for enhanced condition
monitoring
To illustrate the theoretical principles as discussed in the previous section, two examples are given here of prototype enhanced condition monitoring systems. In the first example standard regression models and a simple form of pattern recognition is used to implement model-based FD [Grimmelius cs., 1995]. In the second example artificial neural networks (ANN) are used for the two different types of FD. First, an ANN is trained per measured variable to predict healthy system behaviour, with a separate network to diagnose failure modes from the found residuals: model-based FD. Second, an ANN is trained per fault, to diagnose failure modes directly from the measured data, without calculating healthy system behaviour and residuals: knowledge-based FD [Van Kuilenburg, 19952; Grimmelius cs., 1999].
Both examples apply to the same plant, Figure 1.5, and use the same basic data, Table 1.1. These examples will also be used in section 1.5 to illustrate the main focus of the further research.
1.4.1 Fault diagnosis using regression models and pattern recognition
1.4.1.1 Introduction
In 1993 a prototype diagnostic system for a compressor refrigeration plant was developed. The prototype consists of two separate software modules: a reference module and a diagnostic module, as shown in Figure 1.4. The reference module consists of regression models for all output variables of the plant. These models are based on extensive measurements. The diagnostic module consists of a health monitor and diagnostic module. The knowledge is stored in a qualitative failure mode - symptom pattern matrix. Initially this knowledge was derived
with a Failure Mode and Effect Analysis (FMEA) in combination with interviews with experts. The matrix was however substantially adjusted after measurements became available. The diagnostic technique implemented is based on a fuzzy pattern recognition algorithm. Combining the reference module, the obtained knowledge and the pattern recognition algorithm resulted in a reliable and easy to handle tool.
yn ' $0,n % $1,n@Tchwi % $2,n@Tcwi % $3,n@Z1 % $4,n@Z2 % $5,n@Z3 %
$6,n@log(Tchwi) % $7,n@(Tchwi)&1 % $
8,n@log(Tchwi) % $9,n@(Tcwi)&1 % ,n
(1.1)
Figure 1.5 Lay-out of the plant used in the examples. [Grimmelius cs., 1995]
1.4.1.2 Reference module: the prediction of plant behaviour with regression models
Extensive measurements were performed registering a total of twenty-two variables, shown in Table 1.1 and Figure 1.5. The system boundary is defined around the water chiller, excluding the load and the capacity controller that switches cylinders on and off. During all measurements the mass flows of both the cooling water and the chilled water remained constant, and the influence of the ambient temperature is negligible. Thus the prediction of the reference-values are based on three independent variables: cooling water inlet temperature (Tcwi ), chilled water inlet temperature (Tchwi ) and the number of cylinders in operation (Z). For each of the eighteen output variables separate prediction models were determined. The regression models for the variables contain a maximum of nine coefficients. The models were based on some physical considerations and available regression models for the properties of R22 [Du Pont], Appendix F. The resulting general description of the regression model is:
Where yn is the calculated S reference S value for the dependent variable n, $0, n to $9, n are the regression coefficients and ,n are the unobservable regression errors (due to experimental noise and lack of fit of the chosen model). The Z1, 2, 3 are binary variables indicating the number of cylinders in operation. Estimates for the coefficients ($ˆ0 to $ˆ ) were calculated
9
Suction pressure (PT1) pci Cooling water before condenser (TT8) Tcwi Discharge pressure (PT2) pco Chilled water before evaporator (TT9) Tchwi Pressure after evaporator (PT3) pevo Chilled water after evaporator (TT10) Tchwo Pressure drop over filter (dPT) )pf Crankcase pressure pcrkc
Refrigerant suction (TT1) Tci Oil pressure poil
Refrigerant discharge (TT2) Tco Electrical current compressor I Ic, I Refrigerant after condenser (TT3) Tcondo Electrical current compressor II Ic, II Refrigerant before expansion valve (TT4) Texpi Oil temperature compressor I Toil, I Refrigerant before evaporator (TT5) Tevi Oil temperature compressor II Toil, II Refrigerant after evaporator (TT6) Tevo Control voltage number of acting cylinders Vref Cooling water after condenser (TT7) Tcwo Ambient temperature Tamb
Table 1.1 Measured variables in the examples, gray fields indicate input variables, codes refer to sensors indicated in Figure 1.5. [Grimmelius cs., 1995]
Figure 1.6 Residuals for compressor suction pressure, against time and against calculated value. [Grimmelius cs., 1995]
Plots of residuals (differences between estimated and measured values) are presented in Figure 1.6. The residuals are shown against the estimated value and against time. As can be seen in these plots, the residuals are small, only a few percent, over all the operating conditions.
For most other measured variables similar results were obtained. The relatively large residuals occur at the changing of the number of cylinders in operation. This could be expected since no history terms are incorporated in the regression model, equation (1.1), hence dynamic effects are described poorly. To better show this limitation of these models the results for the oil temperature are shown in Figure 1.7. The obvious lack of fit was caused by the slow dynamics introduced by the use of surface mounted thermocouples on the thick cast iron crankcase.
GMA(t) ' a y(t) % (1&a) GMA(t&1) (1.2)
FGMA . Fy a
2&a (1.3)
Figure 1.7 Residuals for the oil temperature, against time and against calculated value.
[Grimmelius cs., 1995]
1.4.1.3 Diagnostic module: health monitoring and pattern recognition
Before data, either measured or calculated, are used in the health monitor, some preprocessing is performed. First, every cycle with a directly recognizable sensor failure (value out of sensor range) is neglected. Second, in order to prevent temporary disruptions of the measured values to result in an unjustified fault indication, a Geometric Moving Average (GMA) for both the measured and calculated variables is used. A GMA is calculated from the original signal through:
Typical value for a is 0.2 [Himmelblau, 1978], which is also applied here, resulting in a 1 : 4 ratio between the weight of the new data (y(t)) and the old average (GMA(t-1)). The standard deviation of a GMA can be estimated from the standard deviation of the original signal with:
Equation (1.3) is exact if y(t) is normally distributed. For a = 0.2 follows FGMA . Fy /3. The health monitor thus can use thresholds equal to the standard deviation of the regression models to obtain a 99.7% reliability, again assuming a normal distribution of y(t). This is illustrated in Figure 1.8, showing the measured and calculated suction pressure when a failure
Failure mode pi c Tc i pc o Tc o B c poi l Toi l l oil pcrkcPe m )Tsubc)Tcwo -condo )Tcw Texpi)p f pev o )Tsuph)TchwTev i ncyl 1.1.a < - - - - < - - < < - - - - - > > < - -1.2.a > - > - - > - - > - - - - - - > > < - -22 - - > - - - - - - > < - > - - - - - - -3.1.a - - - - - - - - - < - - - < - - > < - -42 > - - - - > - - > > - - - - - > < > - -5.2.b < < < > < - - < < - - < < < < - > < -weight 8 5 5 3 1 8 1 1 3 5 1 1 3 3 5 5 1 5 5 1
1.1.a: Compressor, suction side, increased resistance. 1.2.b: Compressor, discharge side, increased resistance. 2.2.e: Condenser, cooling water side, too little cooling water. 3.1.a: Liquid line, , line and valves, increased resistance. 4.2.e: Expansion valve, controls, bulb no contact with pipe. 5.2.b: Evaporator, chilled water side, too little chilled water.
Table 1.2 Some symptom patterns derived with the FMEA. [Grimmelius cs., 1995]
As final preprocessing, after each change in the number of operating cylinders, four measurement cycles are neglected to exclude transient behaviour. After this initiation phase, a new GMA is developed over another four cycles before a diagnosis is generated.
As already mentioned a Failure Mode and Effect Analysis (FMEA) was carried out. The results were combined with expert knowledge, collected from interviews with system and component designers and service engineers. The starting level for the causal analysis was chosen at the least replaceable unit level, such as for instance the suction valves in the compressor. This led to 58 possible failure modes, for each of which a cause-effect study was carried out. The expected influence on the component and subsequent plant behaviour was then translated into expected measurable deviations of variables, the symptoms. The symptoms have not been restricted to variables generally measured in practice. Some increase in the number of measured variables could be necessary to make a diagnosis possible. In order to use robust sensors and to enable a refit of an existing plant with these sensors, only pressure and temperature sensors and electrical signals have been considered, refer to Table 1.1. The measured variables were either used individually as possible symptoms or translated into variables that can be derived directly from measured variables, such as subcooling, superheat and temperature differences.
The description of component and plant reaction was translated into measurable symptoms, using three ratings:
< : Decreasing value, due to failure mode. - : No effect or unpredictable effect. > : Increasing value, due to failure mode.
Some examples of the resulting symptom patterns are shown in Table 1.2. The failure mode numbers are also used in this research and a full list is given in Tables 4.1 and 4.2 (page 126, 127). In the symptom pattern matrix a weight is associated with each individual symptom in a symptom pattern, shown in Table 1.2 for failure mode 5.2.b only. The weight is determined on the basis of knowledge about the behaviour of the measured variable. A variable that shows a very distinct reaction to the failure mode becomes a higher score than a variable that shows only a limited reaction. Note that a variable that does not change can also be used to discriminate between two failure modes. It proved difficult to predict measurable symptoms from a theoretical analysis alone, so the results for some failure modes have been validated with measurements. This resulted in some adjustments to the failure mode -symptom pattern matrix.
Also, some failure modes are indistinguishable in terms of their respective symptom patterns, because they have identical or empty patterns. Failure modes with identical patterns were combined to a more general fault description. The failure modes with empty patterns can be divided into two groups: those that have no measurable effects and those that only affect transient behaviour. The first category consists for instance of all possible leakages of refrigerant, since this will only affect system behaviour after prolonged occurrence, and should be detected separately in view of environmental legislation. The second category mainly consists of malfunctioning control devices, resulting in instability or slower reaction to changes in ambient and operating conditions. As only steady-state symptoms are taken into account, these failure modes are undetectable by the diagnostic system. As a result, the failure mode - symptom pattern matrix was reduced from 58 failure modes to 37 failure modes, each with a unique symptom pattern.
Failure mode - symptom pattern matrices are discussed in more detail in section 4.3.
The implemented diagnostic technique is a pattern recognition routine, based on multi-valued or "fuzzy" scores per symptom [Van Herwerden, 1993]. After each successful measurement a actual measured symptom pattern is generated. A probability score is evaluated for each failure mode in the matrix, indicating how much the stored symptom pattern is in conformity with the measured pattern. Summing the weights of the matching symptoms, divided by the total sum of the weights per pattern a score is calculated. This score is used for classification of the failure modes as follows:
Score $ 0.9: probable failure mode. 0.5 # Score < 0.9: possible failure mode. Score < 0.5: unlikely failure mode.
Figure 1.9 Typical screen output diagnostic module. [Grimmelius cs., 1995]
indicate whether the lower or upper threshold is exceeded. The small window on the top right side gives a graphical presentation of the selected measured variables.
1.4.1.4 Discussion
The calculated reference values are sufficiently accurate to detect failure modes at an early stage. The implemented fuzzy-pattern recognition routine enables identification of possible failure modes. The probability of a failure mode is based on the similarity between the measured symptom pattern and a predefined failure mode symptom pattern, and not on failure statistics. This makes the system suitable for diagnosing all failure modes, including those that seldom occur. Disadvantages of the presented prototype system are:
< The reference model is based on a simple regression analysis model based on measurements. This implies that for every individual installation a new regression analysis is necessary on basis of measured data.
< It proved difficult to gain the knowledge needed for diagnostic technique. Only by falling back on measured symptoms it was possible to build a reasonably reliable knowledge database.
< The pattern recognition routine is sensitive for failure modes with only a few symptoms. If one of these symptoms occurs, the probability of such a failure mode becomes unrealistically high.
Figure 1.10 A fully connected, three layer feed-forward artificial neural network.
1.4.2 Fault diagnosis using Artificial Neural Networks
1.4.2.1 Introduction
An Artificial Neural Network (ANN) consists of many, simple, but non-linear calculating units, known as neurons, that are connected with each other, as shown in Figure 1.10. The connections between neurons carry numerical information. An adjustable weight is associated with each connection. The behaviour of the network is defined by the function applied in the neurons and the values of the weights. The values of these weights are determined during the training session. There are two ways of training: supervised or unsupervised.
During a supervised training session, examples of the input patterns together with the corresponding output patterns are presented to the neural network. The weights in the network are adjusted using an error function until the results are satisfactory.
During an unsupervised training session, just the input patterns are presented to the network.
The training algorithm is defined in such a way that input patterns will be clustered together if they have the same features.
Application of neural network technology requires large data sets, covering all classes of conditions that the network is required to be able to detect or simulate. A part of the data is used in the training sessions, a different part is used for evaluation of the results, Figure 1.11.
yk '
ö
j nhidden i'1 wk,iö
j ninput j'1 wi,j uj (1.4)healthy condition, and of all the failure modes, preferably for all the different operating conditions.
Fortunately, ANNs have excellent capabilities of generalizing complex non-linear relations, and tend to be very robust to noise in the signals [Kosko, 1992; Mesbahi, 2000]. The final implementation of a feed-forward ANN is simple: the network can be described as a straightforward mathematical equation. Equation (1.4) shows the result for the kth output y
k of an ANN cf. Figure 1.10, with ninput inputs (u) and nhidden neurons in the hidden layer:
However, choosing the most effective combination of the network lay-out, the neuron functions (ö ) and the training method for a given task is difficult. There is little structured
knowledge to help select the best suited network and training method for a certain application. Nevertheless, these choices are crucial for obtaining a good result.
Another important aspect in the application of ANNs is the normalisation of input and output data. The neuron function is limited between [0, 1] for sigmoidal functions, or [-1, +1] for tangent hyperbolic or similar function. To prevent unwanted saturation, all input variables are normalised. The normalisation should be such that comparable stimuli are created from the various inputs. Also the output should be normalised to make comparison possible.
Finally, the initial weights that are used as a starting point for the training have a considerable influence on the speed of the training. Generally small random distributed weights are chosen, to prevent saturation of the neurons.
It is also possible to add history or feed-back terms to the inputs in order to increase the possibilities of the ANN in a dynamic environment. It is also possible to make the network adaptive, in other words keep training the network during application. This can be applied in for instance for complex control applications in changing environments [Mesbahi cs., 1998]. Here only static, strictly feed-forward networks are considered.
ANN-based systems do not provide insight into the classification criteria that the network is trained to use. It is never possible to prove the quality of such a system deterministically. Using statistical techniques (based on a separate test data set), the performance of such a system can be estimated.
1.4.2.2 ANNs used as reference value generator
ANNs are known to have very good fit capabilities, specially for non-linear functions. Instead of the regression models used in section 1.4.1.1., ANNs have been trained to generate reference values for each measured variable. Several different three layer network lay-outs were tested, each with three inputs and one output but varying numbers of hidden neurons. The applied neuron function is the tangent hyperbolic function. Table 1.3 gives a short summary for some of the variables.
For the evaluation of the ANNs, either the measure of fit (R2 ) or the average error is used. The column two through four show the results for the ANNs with the highest measure of fit. The ANNs are identified by the number of neurons in the hidden layer (4 or 6) and the number of iteration (50× or 100×). Column five and six the alternative ANN with the smallest average error (and a lesser measure of fit). In three of the six examples the ANN with the best measure of fit also produced the smallest average error. The last column shows the measure of fit found using a least square fit. The number of iterations was limited to either 50 or 100. It should be emphasized that the training set only consisted of 10% of the available data, where the least-square fit was performed on the whole data-set.Clearly the results are not very promising. It was decided that in view of these results, no further attempt would be made to separately use a ANNs for as reference value generator.
1.4.2.3 ANNs used as enhanced condition monitoring system
Even if the expected symptoms caused by a failure mode are known from a-priori knowledge, for instance through the use of a detailed model or through measurements, it is still often difficult to discern them in the measured signals. In this case ANNs are used to generate a diagnosis direct from the measured data, without first assessing the symptoms. The measured data included the introduction of seventeen failure modes. To enhance the flexibility and to be able to add more failure modes in the future, a separate ANN was trained to recognise each fault. Each network is trained with a different training data set. All networks have eleven input neurons and two output neurons. The number of hidden neurons varies, depending on the fault to be recognised.
The results of the different networks are shown in Figure 1.12. This figure shows the percentage of good classification of the test data set for the ANNs trained to detect the
Variable minimum RNetwork 2 R2 averageerror Network minimumaverage error averageerror Least-squares fitR2
pci 4 - 100× 71 3.3% 6 - 100× 2.96% 97 pcond 4 - 50× 88 2.2% idem 98 Tco 4 - 50× 78 3.4% 6 - 50× 3.2% 95 Tevo 6 - 50× 87 16.3% 4 - 50× 15.2% 98 Tcwo 4 - 50× 82 2.6% idem 98 Tchwo 6 - 100× 95 9.4% idem 99
0,7 0,75 0,8 0,85 0,9 0,95 1 1,05 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 fault number
percentage of good classifications
normal fault cross
Figure 1.12 Analysis of the results with direct diagnosis from data using neural networks.
[Grimmelius cs., 1999]
< Secondly, when a certain fault has been introduced into the plant, the corresponding ANN should recognise this fault. Shown with the middle column per fault number this results in a somewhat lower percentage. The lower score is mostly due to the fact that the data files which contain the data of the failure modes, also contain some healthy system data.
< Finally, if a certain fault occurs, only the corresponding ANN should react, the others should remain unaffected. Shown with the right column per fault, the cross result score is on average a little lower then the direct fault recognition score, due to the fact that some failure modes are prompting two or more networks to report a fault. In all cases this cross disturbance reduces as the fault becomes more pronounced.
The performance of the ANNs shown here is calculated without post processing. When using an appropriate post processing method, such as a majority vote and/or thresholds, the results will improve. Another point to be taken into account is that it is important to recognise a fault before serious damage to the machinery has occurred. This implies that a wrong diagnosis at an early stage is not necessarily a problem, if correctly S i.e. associated with a low reliability level S presented to the human operator. The operator is thus warned that something is not correct, and will be alert to the diagnosis as it becomes more reliable or will be able to do additional inquiries to confirm the warning.
1.4.2.4 Discussion
The results from the application of ANN are promising from a data processing point of view: little knowledge of the plant or it's condition is needed to train ANNs to either generate reference values or discern pre-defined conditions. Also in more difficult applications, including dynamic behaviour and control algorithms good results have been reported [Mesbahi, 2000].
In short, the main advantages are:
< Neural networks are very fast. This is useful when a diagnosis system must run in real time and must handle a lot of signals.
< Little or no process knowledge is required using ANNs. < No knowledge about fault symptom is required.
< An ANN is robust, especially regarding noise.
These advantages are counterbalanced by the some important disadvantages: < No fixed rules concerning network lay-out and training methods exist.
< Therefore, knowledge about, and a "feel" for, the building and training of ANNs is required.
< An extensive set of measured data is required for all classes of conditions, including failure modes.
< An ANN does not provide insight into the criteria it uses to classify the input patterns, because the learned knowledge is distributed over all the weights in the network.
< An ANN only provides valid answers inside the trained range.
< An ANN, once trained, is not flexible. If environmental conditions change to values outside the trained range, the network must be retrained.
< The required data sets can be very large. < The training can be time consuming.
1.5
Research approach
1.5.1 Focus
The general research assignment for this project is:
'To generate knowledge required for the condition monitoring and fault diagnosis of compression refrigeration systems on board ships.'
To focus the research, an initial assessment of the required and the already available knowledge is necessary. To be able to build an enhanced condition monitoring system, knowledge of all elements shown in Figure 1.2 is necessary. As discussed in section 1.3 a fault detection system will promptly react to all faults with the correct diagnosis, without reporting faults when there are none. Adequate validation of sensor readings is needed to prevent unwarranted fault messages. An accurate reference value generator will result in reliable residuals for all possible operating conditions. The health monitoring should be neither be too sensitive, resulting in unwarranted fault messages, nor too robust, resulting in missed or late fault messages. The diagnosis itself should be effective and reliable, and make adequate use of the knowledge available in the database. The knowledge sources should give enough reliable data to construct a comprehensive database.
The first example discussed in this chapter, based on previous research, clearly shows that the knowledge needed to diagnose a fault is difficult to obtain, either as heuristic knowledge (from FMEA, interviews etcetera) or as measured behaviour. The diagnostic technique itself used in the example is also not ideal. Though sensor validation, reference value generation, and health monitoring did not give direct problems, improvement is certainly possible.
Thus the very broad formulation of the research goal is narrowed down to two main topics: knowledge or prediction of faulty behaviour, and an effective diagnostic strategy.
The second example, based on research performed within this project, shows that other techniques for diagnosis, in this case artificial neural networks, are available, and yield promising results. Other research indicates that a diagnostic strategy employing a combination of techniques may prove to be more effective [Grimmelius c.s., 1999]. The main issue however remains the knowledge of the faulty behaviour of the plant, essential for a correct diagnosis. Therefore this research is limited to generating knowledge on the faulty behaviour of compression refrigeration plants.
1.5.2 Challenge
There are many design variations possible in compression refrigeration plants. There is a distinct influence of the lay-out of the plant on the faulty behaviour, hindering the portability of knowledge between different plants. A generic method for obtaining the required knowledge for a specific plant is therefore sought.
Another important prerequisite is the availability of the knowledge at the moment of commissioning of the plant. There is limited time available for testing and adjusting the plant at commissioning and adding extensive faulty behaviour analysis is certainly unwanted, and often impossible. The knowledge should already be available at commissioning, using only the data available during the design of the plant.
An additional challenge is the wish to be able to detect failures, and diagnose the failure modes, already at an early stage, to make corrective action possible such to prevent further damage and to enable a planning of the repairs.
The techniques available to try and accomplish this can be divided into three categories: < Heuristic analysis, such as FMEA and Fault Tree Analysis.
< Expert consulting through interviews, test-cases and observation. < First principle analysis, using the basic describing physics of the plant.
In the first example discussed in this chapter, the first two methods were applied, and the results were not convincing. Both techniques show important flaws, especially when they are applied to newly designed or otherwise not well known plants.
One way to employ first principle knowledge is the development of time-based simulation models. Simulation models are available for many components, but few are capable of simulating faulty behaviour since the models are mainly applied for design or control purposes, where there is little use for extensive faulty behaviour simulation. Furthermore, it should be possible to define the models in the design stage of the plant.
1.5.3 Hypothesis
From the previous sections, the original research assignment is restated in a research hypothesis:
'It is possible to design simulation models suitable to reliably predict qualitative symptom patterns of failure modes for compression refrigeration plants, based on manufacturers data
1.5.4 Limitation
For faulty behaviour prediction as described above, there is no apparent difference between refrigeration plants installed on ships and those installed on shore. So no ship specific components will be used. The viability of the approach will be demonstrated using the most simple compression refrigeration plant to limit the time needed for the development of the models.
