University of Technology and Life Sciences
Summary
In practical applications of machinery condition monitoring, in terms of func-tional reliability as well as in terms of physical diagnosis occurs at the level of the maintenance of machinery. In the process of operation, supported with diagnostic methods, taking into account the nature of changes in the value of excitations (loads) influencing an object the following are defined: accidental damage (sudden), which is caused by abrupt stimuli that cause values to exceed the established parameters of proper work; wear and tear damage (natural), as a result of irreversible changes in initial properties of an object that occur during the exploitation as a result of dam-age.
Keywords: state, machine, damage, monitoring, exploitation, diagnostic 1. Introduction
The processes of destruction of technical systems necessitate the need to monitor changes in their condition [1,2,3,4,10,12,16,17,18,19,20].
Methods and means of modern technical diagnostics are a tool of diagnosis of their condition, which is the basis for decision making [1,4,6,12,20].
Confronting revised requirements and new opportunities generated a new class of research problems, intensified others, while many directions of research became irrelevant without the possibility of application through: – access to the advanced world of technology – possibility of buying the latest generation of test equipment and electronic components – possibilities of the latest IT applications in the field of hardware and software – database access, equity and broad possibilities of cooperative relations. All of these elements dramatically changes views and achievements in range of detection and monitoring of changes in the methods of technical diagnos-tics of objects, especially mechatronic objects. This gives a possibility to monitor changes in condition and location of damage and minimize the effects of damage.
2. Changes in machinery condition
In mechanical engineering, developing technical diagnosis basing primarily on the use of in-formation about the changing machine state can monitor the progressive destruction of a machine throughout its lifecycle. Condition changes – mapped by technical diagnostics methods - prevent causes and effects of damage. Damage is one of the important events occurring in the use of machines, determining the reliability of machines, efficiency of their utilization, technical support process and the range of technical diagnostics.
Generally, the concept of machine damage can be defined as an event consisting in the transi-tion of a machine (group, element) from the state of fitness to a state of unfitness. The state of fitness is understood as a machine state which meets the designated features and retains the
pa-rameters specified in the technical documentation. However, the state of unfitness means a machine state which does not meet even one of the requirements specified in the technical documentation.
As a result of interaction of the environment and the implementation of the task imposed on an object initial properties of an object may be changed, which reflects a change in the value baseline characteristics, and, possibly, a measurable change in the state of unmeasured characteris-tics. Damage to machinery in operation can proceed as follows:
– due to the slow, irreversible aging process and wear occurring in a machine
– due to the emergence of a reversible process of varying intensity, caused by temporary crossing of acceptable values of one or more of forcing factors
– in jumps, manifesting itself in discontinuous moving of one or more features beyond the ac-cepted limits of a machine.
Given the foregoing observations, one may indicate the main causes of failures, which are classified as follows:
a) construction – damages due to the errors in the design and construction of a building, mostly by not taking into account extreme burden, i.e. values that significantly exceed the nominal load, leading directly to damage;
b) Production (Technology) – damage caused by errors and inaccuracies in processes (lack of dimensional tolerance, surface roughness, thermal treatment, etc.) or defects in material elements of an object;
c) operational – damages as a result of not complying with the applicable rules of operation or as a result of unforeseen external factors or impact conditions for the usage of an object, which leads to the weakness and premature wear and tear of achievements of the boundary condition
d) aging and wear – always accompany the use of objects and are result of irreversible changes that lead to the deterioration of strength and ability to cooperate of individual items.
Damage or destruction of a technical object takes place under the influence of its transferred energy. Depending on what kind of energy dominates in certain circumstances, the causes of damage to components can be divided into the following groups:
a) mechanical – static stress, creep, fatigue, pitting, abrasive wear, b) chemical – metal corrosion, aging of rubber, paint, insulation, rotting wood, c) electric electrocorrosion
d) heat – overmelting, intensifying the course of phenomena. 3. Classification of damage
For proper characterization of property changes and phenomena that cause them and occurring in machines during their operation, especially the events leading to the formation of defects, reliable data about the features of various teams and working conditions of their work are needed, which involves the need for classification of equipment.
In technical equipment, the following can be distinguished:
* active elements, which are directly involved in energy metabolism, power transmission, processing, types of movements working on other kinds of them, carrying loads, etc.;
* base elements which determine the correct placement of active elements and supporting elements, such as bodies, guides, frames,
* supporting elements, which protect a device from overload or over-limit states.
The clarification of utility functions and conduct of classification of the characteristics (prop-erties) of an object is possible with the use of methods of technical diagnostics. The most widely used is the following division of characteristics:
* Characteristics of the critical factors determining the level of threat to life or human health, environmental risks, risk of co-operating systems and a complete loss of use value of an object (product), subject to monitoring;
* important features, which have significant importance for the assessment of the state (fitness) of an object, identifying hazards for structures reversibly changing during the operation;
* non-important characteristics, resulting in minor and reversible decrease in the effec-tiveness of a facility.
The presented features due to the method of assessment can be divided into:
* measurable characteristics, which can be measured and their nominal value and the limit can be defined;
* unmeasurable characteristics, the assessment of which shall be made only organolepti-cally.
Evaluation of the critical features is carried out mostly in the form of monitoring in relation to each of them separately and provides a basis to exclude the object of life, while not meeting the requirements of any of the features. Nominal values and limits for these traits are determined by relevant standards, or are determined by a user.
Important features are a basis for assessing the current state of an examined object, and deter-mine the scope and need for maintenance activities and repairs.
Damage, depending on the nature of the show, can be divided into [ 28]:
1) primary (independent), or those whose appearance was not caused by other damage, 2) derivative (dependent), if damage of one device was caused by damage in another device, 3) combined, that is damage of separate components of the same device occurring at the same
time,
4) single, when they appear separately,
5) gradual, which emerge as a result of changes in the course of these parameters, which de-termine the moment of injury due to the interaction of various processes, such as physical, chemical, etc.
6) sudden, characterized by displacement, an unacceptable change of the essential characteris-tics of components, equipment, etc.; nothing influences the likelihood of their occurrence – neither the number of working hours nor the calendar period.
From the perspective of causes, damage can be divided into:
1) random with a constant risk of exploitation in the process; subject to such damage are sub-jects whose condition does not depend on the time of operation,
2) caused by errors in manufacturing and servicing with a fading risk of appearance in the op-eration process; they occur most frequently in the initial period of opop-eration,
3) due to wear and tear and aging processes of the elements of a growing risk in the operation process; they occur mainly in the final period of operation,
4) caused by not observing the assumed operating conditions, such as congestion of different nature; the distribution of this type of damage over time is generally unknown; a constant risk of their occurrence in the process of exploitation is assumed most often.
4. Damage prevention
The reduction of the destructive impact of physical aging and use of mechanical objects is necessary in all phases of facility existence. Measurable effects of reducing the number of failures of technical objects can be shaped:
* in the field of design – proper selection of materials and shapes to loads, the development of unit pressure, selection of materials for friction pairs, the elimination of dry friction, the ex-tensive use of appropriate seals, providing suitable temperature,
* in the field of technology – by selecting an optimal type of treatment, optimal shape of the surface layer, the selection of appropriate heat and thermo-chemical treatment, proper assem-bly and regulations;
* in the area of operation – by observing the frequency and extent of maintenance activities (lu-brication, adjustments, corrosion protection), avoiding overloads and sudden changes in speed, status monitoring.
Generally, the methodology of machine damage prevention distinguishes between two groups of methods:
– pre-operational methods used in the phases of development (evaluation), design and manu-facture of machines, with a clear indication that they are most efficient in economic terms – operational methods used during operation, even if such methods are not provided for in the
development process.
At the design stage, the characteristics of machine parts are determined through deciding on their shapes and sizes of materials which will be executed, tolerance, surface roughness and the accuracy of their mutual connection. The design documentation shall also includes the durability of the material, the type of geometric structure of the surface, and sometimes the way of handling an element.
When designing a machine, the danger of damage by staff members has to be reduced to a minimum. Simplification, typification and standardization of components and mechanical systems leads not only to good reliability, but also reduces costs and simplifies the construction.
Operational prevention methods include:
– rational exploitation of machines in given conditions and specific purpose,
– testing and monitoring of the developing damage with the use of diagnostic methods, – compliance with the requirements of technical and mobility documentation in the scope of
frequency and technical activities
– statistical examination of damage in operation for the modernization (redesign) of machin-ery, spare parts management efficiency, etc.
Incorrect exploitation causes the processes of intense wear and tear, leading to premature damage.
5. Effects of damage – damage statistics
During the operation of variable environmental conditions and loading, frequent running, and work in a wide range of engine speed and load consumption accelerate the processes leading to the formation of pollution damage. For example, ADAC [19] reported that 25.7% of damage is attrib-utable to engine component damage (the rest falls on the engine design, such as fuel system, fuel injection, cooling, etc.). Damage to engine components is distributed as follows:
pistons and connecting rods – 24.1%, crankshafts – 18.5%,
cylinder liners – 16.1%, bearings – 14.9%, housing (body) – 9.6% cylinder head – 7.6%,
the elements of timing – 5.8%, others – 5.4%.
Technical damage of a vehicle to a large extent affects the safety of a driver, as well as creates danger to other road users. It was found that technical damage in vehicles constitutes approxi-mately 2% of all accidents, of which:
– brake system – 25% – tires – 20%
– light – 14%
– steering and suspension – 7.5%.
The number of accidents caused by technical defects is estimated to increase by 7%. This is due to the lack of concern for the vehicle condition (the cause of approximately 25% of technical failures).
Frequent occurrence of damage is manifested by reduced quality of service vehicle. This af-fects significantly the lead time and the comfort of a vehicle. Long and short recovery periods between repairs make a vehicle a subject to more repair than operation for which it is intended.
Lowering the quality of our services due to damage to vehicles also entails significant effects of economic nature. Too long lead times, bad comfort of travelling and low rate of technical readiness of vehicles cause the loss of a customer, and thus lower profits from their activities. Many companies providing vehicles in recent years investigated the problem of the intensity of damage to vehicles, sensing the cause of low competitiveness here. These companies invested a lot in the purchase of rolling stock of high quality, entrusted it to experienced drivers, and operation is conducted at authorized service stations. This resulted in the emergence of highly competitive and backed by high quality services, thereby improving finances of these companies.
6. Diagnostics in damage development evaluation
Technical diagnostics includes the following forms of action: 1 condition assessment,
2 condition forecast,
3 condition genesis – explored in the smallest degree.
These forms of action are implemented by intelligent diagnostic systems (mobile software and hardware, loop-learning and risk assessment).
In the study of the objects we use physical or symbolic models, which are physical or mental representation of the original test.
Modelling for diagnosis includes physical, mathematical and energy modelling, which gives the basics of symptom, holistic and energy diagnostics.
The main problems of machine diagnostics include: – acquisition and processing of diagnostic information; – building models and relationships of diagnostic reasoning – diagnostic reasoning and limits;
– classification of machine states; – anticipating the next time of diagnosis; – imaging information, decision-making.
These thematic groups are the area of interest in the techniques and methodology of forming and sustaining the quality of the machinery, which is conditioned by the dynamic development of the following issues:
– modelling objects,
– methods of diagnosis, genesis and forecast
– susceptibility diagnostics (friendly methods and objects), – construction of economical and accurate means of testing,
– the possibility of experiments in successive phases of machine existence – the methods of assessing the effectiveness of research method application – the methodology of the design and implementation of measurement systems – artificial intelligence methods in research.
Diagnostic Signals
Physics of phenomena accompanying the work of each machine based on the model of signal generation is the foundation of a good diagnosis and is based on knowledge of the description of machine dynamics, which facilitates a smooth transition to the area of diagnostics (BEM, FEM, MSES, AM). napĊd u x y T 2T T T 2T t t t T - okresowa transformacja Układ kinematyczna dynamiczny ϕi(t,θ,r) x(t,θ)=Σϕi∗δri h(t,θ,r) y=Σh∗ϕi∗δri
Fig. 1. Model of machine diagnostic signal generation
Diagnostic parameter selection
A set of diagnostic parameters of the signal is distinguished from a set of output parameters associated with a machine. Determining a set of damage sensitive diagnostic parameters should include:
–the ability to map changes in the status during operation,
–the amount of information about the technical condition of transmission, –the sensitivity of parameters during operation.
Methods of determining diagnostic symptoms are the following:
The method of maximum sensitivity of a parameter to the change the condition. The method of maximum relative changes in a diagnostic parameter.
The method of maximum capacity of diagnostic parameter information. The method of maximum variability of a diagnostic parameter.
The advantage of these methods is that they allow you to choose from a set of output pa-rameters one-piece and multi-element sets of diagnostic papa-rameters.
The criteria for optimization of a set of diagnostic parameters:
1 Diagnostic parameters should be characterized by a process of destruction of the facility and should be closely associated with him
2. Diagnostic parameters should be sensitive to changes in the ongoing process of deterio-ration of the suitability of an object
3. The number of diagnostic parameters cannot be too large because a substantial number of them makes it difficult and sometimes impossible to know the deterioration of an object
4. Diagnostic parameters should be measurable
5. There must be reliable statistical and analytical data of chosen parameters (BEDIND, SVD, PCA).
The problems of technical diagnostics
Technical diagnostics is developing in two directions:
– development of methods of examination of an object (structure, functioning, physical and chemical processes, models of signal generation)
– diagnostic process planning (a generalization of formalization: the description of diag-nostic procedures, methods of optimization – diagdiag-nostic casts, diagdiag-nostic programs, collections checks).
This gives an answer – how to best examine an object?
Diagnostic system optimization problems (forces and resources to carry out the testing process) are analyzed less frequently. These include: organization structure of control and meas-urement of fixed and variable programs, diagnosis, selection of measmeas-urement methods and instru-ments, determining end relations, the presentation of the diagnostic system, etc.
A diagnostic system is therefore an object of separate consideration, and diagnostic char-acteristics of such systems need to be developed and described (with mathematical formalization).
Recent topics of diagnosis rapidly developing include: 1 modern methods of signal processing,
2 multidimensional diagnostics of machines; 3 numerical analysis and synchronous methods; 4 energy diagnosis;
5 diagnosis by identification; 6 diagnosis according to model;
7 elements of artificial intelligence in diagnosis; 8 modern information technology in diagnosis; 9 design of computer diagnostic systems; 10 intelligent diagnostic agent.
The problems of technical diagnostics:
1. The time of constituting a diagnostic symptom;
3. A comprehensive assessment of the state: – measurement,
– a reference to the limit, – forecasting the state,
– setting a deadline for another diagnosis,
– the genesis of value change causes of a measured symptom;
4. Supervision of the developing damage (fault tolerance, STOP – for critical damage acc. to Sgr)
5. PSOT – preventive system for technical service with adaptive interference. 7. Diagnostic system
A diagnostic system is a set of elements and relationships that are essential in the process of diagnosis. Since the process consists of a series of actions which result in the conversion of infor-mation about properties of an object into inforinfor-mation about its condition, the form of the diagnos-tic system is dependent on the type of facility and diagnosdiagnos-tic activities necessary to develop a diagnosis.
A detailed definition of a diagnostic system is as follows:
“A DIAGNOSTIC SYSTEM is a team of diagnosticians, a collection of methods and means for obtaining, processing, presenting and collecting information and a collection of objects, their models and algorithms for diagnosis, prognosis and genesis of states, as well as the relationship between these elements, designed to make reliable decisions about belonging of a tested object to a particular class of states.”
The structure of a diagnostic system proposed in Figure 2 shows the basic relationship be-tween the object of study, the diagnostic model and system diagnosis and decision.
Tidying up the system structure is expressed by a set of relationships and concerns of some properties of its components, which results in distinguishing between various structures, e.g.: organizational, economic, technological, etc. Diagnostic systems belong to a class of information systems and are distinguished by the fact that:
* the purpose of their actions is determining the status of other objects (or systems), virtually without affecting the change of this state,
* this purpose is an elaboration of diagnosis, which is possible thanks to the processing of in-formation about the properties of an object to the inin-formation about its condition.
For these reasons, the main attention should be given the information structure of a diagnostic system; it should be designed, optimized and evaluated with reference to the flow of information.
Different forms of constituents and their different use in the system makes it possible to create diagnostic systems with different structures and varying degrees of automation.
A. A non-automated diagnostic system includes an individual (or a group of people), which performs all the operations, using measuring instruments and methods of collecting and processing information about a tested object, and generates a diagnosis, which is registered if necessary (e.g. in a protocol). Such a system includes a measuring instrument, diagnostician and guide data.
N maszyn (N > m) bądĨ obserwacji S > m niezaleĪnych dyskryminant tej samej maszyny w kolejnych lub symptomów sygnału stanach eksploatacyjnych diagnostycznego m - rozróĪnialnych uszkodzeĔ poszukiwanie niezaleĪnych o róĪnej intensywnoĞci symptomów
obiekt diagnostyki i jego model decyzja o decyzja o klasie stanie jakoĞci obiektu rozpoznania stanu i decyzji Model stanu obiektu badaĔ. Model obserwacji diagnostycznej. Algorytmy klasyfikacji obiektów. Układ decyzyjny. zadane lub nieznane kryterium jakoĞci wzorzec dopuszczal-nych stanów
Fig. 2. Structure of the diagnostic system
B. Automated diagnostic system uses an arrangement of technical devices, which con-duct in the process of diagnosis in accordance with the specified program. Human intervention is minimal, mostly it is about turning the system on. Automatic diagnostic systems are usually covered by self-control, and the existence of damage is indicated. In such a case spare elements can be switched on, or the controlled object is excluded from operation. Individual results of control or just the results crossing the limits are recorded automatically.
8. Condition assessment of a mechatronic system
A mechatronic system is a modern machine composed of mechanical, electronic and electrical parts, integrated with modern information technologies in the field of creations, as well as accom-panying processes. Systems to monitor the status of such systems are essential equipment, protect-ing against the occurrence of damage or malfunction. Algorithmic solutions organize the acquisi-tion process of these systems, order and process measurement data according to established rules, supporting the classification of states, or diagnostic decision support. Equipment solutions define
hardware multichannel measuring structures with any sensors, control structures for their measur-ing circuits, connections integratmeasur-ing sensors with various transducers or implementmeasur-ing systems, detecting danger, alarm, or switching-off.
Mechatronic systems and their development, as the next stage of the development of the qual-ity of construction machinery, is closely connected with the development of cybernetics and general systems theory and information theory and management.
They work on these systems from the point of view of complexity of relationships, networks and feedback within the system, mechanisms of stability, growth, self-collection and processing of information.
Mechatronics is a foreground of the development of bionics, which examines the principles of construction and operation of biological systems in order to construct possible mechanisms, machinery and technical equipment, whose characteristics are similar to the characteristics of living systems. In addition to mechatronics, biomechanics, and neurobionics, bioenergetics is developing, which enables the formation of hybrid technical systems consisting of mechanical and electronic parts and a living organism.
The challenges mentioned above should be faced by future technical diagnostics, for which new challenges should be foreseen right now. The current status of the development of technical diagnostics of mechatronic systems is only the beginning of many new, not always explored substantive and methodological challenges.
Monitoring systems of such systems are based on sensors of changes in a sub-source, smart sensors, collection of information systems and an operator station. The main element of such systems are smart transducers which include: acquisition block, processing-control block and block of communication with the environment. Their advantages in comparison with the previous generation of instruments are as follows:
– the ability to develop measurement procedures in a digital form
– the feasibility of processing algorithms without changing the structure of an instrument – the possibility of communication involving the use of specialized interfaces for generating
measurement information and control decisions.
It all contributes to the fact that intelligent converters have adaptive characteristics, on the ba-sis of measurement conditions, properties, requirements and restrictions allowing the choice of measurement algorithm according to the study of the problem. In converter memory there is certain software of algorithms and a program for their choice. The choice is conditioned according to the performed function, accumulated knowledge and information about the conditions of meas-urement.
The most generally understood diagnosis of such systems within its scope covers all activities related to:
– diagnostic observation of objects,
– processing data collected in the observation process in order to obtain diagnostic input data for the process of diagnostic inference,
– conducting various numerical experiments supporting diagnostic reasoning,
– diagnostic prevention, the results of which include appropriate action plans to restore the full usefulness of diagnosed objects and other repair and correction activities (e.g., plans of staff training, upgrading equipment and technology used, etc),
– sharing the results of diagnostic tests with appropriate groups of recipients, especially those governing the operation of a group of technical measures. The factor that links all of these steps into one is modern information technology.
Such systems (SCADA) may consist of several levels:
The level of measurement sensors which process the various process data (temperature, voltage, current, power, pressure, vibration, etc.) to an electrical signal.
The level of PLC drivers PLC can create a master-slave structure, where one can manage the work of others. This allows you to create complex structures for measuring and control.
The level of PLC connections with computer systems PLC can be connected to computer systems via industrial networks or local area network.
The level of data station A station data collects data from sensors attached to the driver SCADA. It is possible to process thousands of measurement values.
The level of local area networks This level of client stations can be combined with data sta-tion, allowing access to process data from the workstations located in different areas of an organi-zation.
The level of client stations presenting measurement values in the form of synoptic images. Diagnostic systems have the ability to retrieve information from a tested object. They also possess a capacity to process the form of collected information until a diagnosis is formulated. From the information point of view, each element of the system thus has two inputs and one output.
9. Technical diagnostics
In practical applications of machinery condition monitoring, in terms of functional reliability (treated as an ability of machines to perform tasks), as well as in terms of physical diagnosis (identifying the causes of the damage) occurs at the level of the maintenance of machinery. In the process of operation, supported with diagnostic methods, taking into account the nature of changes in the value of excitations (loads) influencing an object the following are defined:
– accidental damage (sudden), which is caused by abrupt stimuli that cause values to exceed the established parameters of proper work; sudden failures cannot be predicted on the basis of technical service results, including the diagnosis;
– wear and tear damage (natural), as a result of irreversible changes in initial properties of an object that occur during the exploitation as a result of aging and wear and tear; wear and tear damage arises from stimuli that accumulate during the use and can be expected, given the re-sults of diagnostic measurements.
The practice of diagnostic applications covers many important areas such as: 1. Diagnostic system organization (DSEM)
2. Management and quality systems (TQM, TPM) 3. Modern information technology (ISZOT)
4. Modelling service systems to the diagnosis and risk assessment.
In practice of enterprises, the problems of operation and diagnostics are built in the computer functioning systems – Fig. 3.
Descriptors of the diagnostic system of machine existence
Mere functioning of a machine diagnostic system, from the point of view of methodology for the use of technical diagnostic tools and ongoing evaluation and machine condition prediction requires the knowledge of:
– symptoms of machine condition: : s1, s2, ..., sm; – limit values of symptoms:
A
P
s
S
gr g2
σ
+
=
− ;- – the periodicity of diagnostic tests: m
m m gr g D
S
S
S
P
T
=
(
1
−
)(
−
)
θ
.The knowledge of the technical state in so functioning exploitation system (DSEM) is the ba-sis for making exploitation decisions: about further use, referral for technical service or liquida-tion. This points to strictly necessary purchases of materials or spare parts, planning the scope of repair station operation, as well as investment purchases.
N O W O C Z E S N E T E C H N O L O G I E T R A N S P O R T W Y T W Ó R C Z E D Y S T R Y B U C J A z e l e m . s z t . i n t e l i g . ( R O B O T Y , M A N I P ) S Y S T E M Y I N F . S Y S T E M I N F O R - W Y T W A R Z A N I A M A T Y C Z N Y Z A R Z Ą D Z A N I E O P I S P R O C E S Ó W N A R Z ĉ D Z I A p r z e z : J A K O ĝ û P R A C E B I U R O W E R A C J O N A L N E J B E Z P I E C Z E ē S T W O K A L K U L A C J E E K S P L O A T A C J I ĝ R O D O W I S K O O P T Y M A L I Z A C J A S Y S T E M I N F O R M A T Y C Z N Y I N F . S T R A T E G I I M A S Z Y N Y F I N A N S E - K S I ĉ G O W O ĝ û M E T O D Y D I A G N O S T Y K A M A S Z Y N D A N E P E R S O N A L N E ĝ R O D K I S Y S T E M U T R Z Y M A N I A M A S Z Y N S P R Z E D A ĩ T R A N S M I S J A S T R A T E G I E E K S P L O A T A C J I U B E Z P I E C Z E N I A Z A S I L A N I E - L O G I S T Y K A P L A N K O N T R O L I A U T O M A T Y Z A C J A I N F O R M A T Y Z A C J A I N F O R M A T Y Z A C J A E K S P L O A T A C J I M A S Z Y N Z A K Ł A D U I N D Y W I D U A L N Y P R O G R A M P R Z E D S I ĉ B I O R S T W A S Y S T E M I N F O R M A T Y C Z N Y P R Z E D S I ĉ B I O R S T W A ( S I P )
Fig. 3. Exploitation spot in IT system of an enterprise
10. Conclusion
Shaping and quality assessment of machines with the use of methods of technical diagnostics is closely connected with the need to maintain an adequate level of their functional characteristics in certain conditions. For the purpose of distinguishing, evaluating and maintaining characteristics the following are used:
– the possibility of technical diagnostics, including the construction of diagnostic evaluation of product quality, operational diagnostics, methods and means of technical diagnostics, diagnos-tic tests to support computer technology,
– reliability testing in the phases of: pre-production, production and post-production, with the use of bench studies, modelling, deterministic and stochastic forcing factors, computer-aided reliability tests,
– methodology of shaping ”quality” with the use of “quality control system of a company” to take account of the quality standards as per EN 29 000;
– service technologicality and repair testing, and corrective maintenance of vehicles, to enhance the intensity of aging and wear and tear of the elements shaping the vulnerability and to evaluate the effectiveness of vehicle operation.
These thematic groups are the area of interest of a wide range of community service, contrib-uting to the development of methods and methodology development and the maintenance of vehicle fitness.
Technical diagnostics, alongside tribology, reliability theory, safety and exploitation theory is one of the fundamental teachings referring to rational exploitation of objects. Understanding physical phenomena occurring during the operation of a machine allows you to specify the qualita-tive relationships between destrucqualita-tive processes taking place and machine condition. A large dispersion of initial properties of a machine, as well as uncertainty and continuity of the aging process clearly mark the objectives and tasks of diagnostics of machines that must work out a specific set of methods and means of diagnosis.
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MONITOROWANIE STANU MASZYN KRYTYCZNYCH Streszczenie
Zmiany stanu maszyn krytycznych wymagają dobrze zorganizowanego systemu ich eksploatacji. Degradacja stanu maszyn wymaga wprowadzania diagnostycznych systemów eksploatacji maszyn. Nowoczesne technologie diagnostyczne pozwalają oceniaü zmiany stanu maszyn oraz umoĪliwiają podejmowanie racjonalnych decyzji eksploatacyjnych. W pracy przedstawiono deskryptory diagnostycznego systemu eks-ploatacji maszyn krytycznych.
Słowa kluczowe: eksploatacja, diagnostyka, maszyny krytyczne, uszkodzenia
*This paper is a part of WND-POIG.01.03.01-00-212/09 project. Bogdan ĩółtowski
University of Technology and Life Sciences Faculty of Mechanical Engineering
