Condition Monitoring of Rotating Electrical Machines (3rd Edition)
2: School of Engineering, University of Warwick, Coventry, UK
3: Department of Engineering, Durham University, Durham, UK
Condition monitoring of engineering plants has increased in importance as engineering processes have become increasingly automated. However, electrical machinery usually receives attention only at infrequent intervals when the plant or the electricity generator is shut down. The economics of industry have been changing, placing ever more emphasis on the importance of reliable operation of the plants. Electronics and software in instrumentation, computers, and digital signal processors have improved our ability to analyse machinery online. Condition monitoring is now being applied to a range of systems from fault-tolerant drives of a few hundred watts to machinery of a few hundred MW in major plants. This book covers a large range of machines and their condition monitoring. This 3rd edition builds on the 2nd edition through a major revision, update of chapters and a comprehensive list of references rotating electrical machines; electrical machine construction, operation and failure modes; reliability of machines and typical failure rates; signal processing and instrumentation requirements; on-line temperature monitoring; on-line chemical monitoring; on-line vibration monitoring; on-line current, flux and power monitoring; on-line partial discharge (PD) electrical monitoring; on-line variable speed drive machine monitoring; off-line monitoring; condition-based maintenance and asset management; application of artificial intelligence techniques to CM; and safety, training and qualification.
Inspec keywords: partial discharge measurement; asset management; power measurement; vibration measurement; safety; training; qualifications; chemical variables measurement; condition monitoring; electric machines; electric current measurement; machine testing; electrical maintenance; temperature measurement; vibrational signal processing; reliability; variable speed drives; signal processing equipment; failure analysis; artificial intelligence
Other keywords: online chemical monitoring; training; machine reliability; condition-based maintenance; online vibration monitoring; rotating electrical machines; power monitoring; flux monitoring; failure rates; online temperature monitoring; safety; current monitoring; qualification; electrical machine construction; electrical machine operation; signal processing instrumentation; online PD electrical monitoring; asset management; condition monitoring; variable speed drive machine monitoring; offline monitoring; electrical machine failure modes; artificial intelligence techniques; partial discharge electrical monitoring
Subjects: Civil and mechanical engineering computing; Nonelectric variables measurement methods; Drives; Artificial intelligence (theory); Education and training; Testing; d.c. machines; Vibrations and shock waves (mechanical engineering); Mechanical engineering applications of IT; a.c. machines; Computerised instrumentation; Electric and magnetic variables measurement methods; Products and commodities; Instrumentation; General and management topics; Mechanical drives and transmissions; Reliability; Health and safety aspects; Engineering mechanics; Plant engineering, maintenance and safety; General electrical engineering topics; Measurement; Production facilities and engineering; Signal processing and conditioning equipment and techniques; Dielectric breakdown and discharges; Maintenance and reliability; Education and training; Expert systems and other AI software and techniques; Inspection and quality control
- Book DOI: 10.1049/PBPO145E
- Chapter DOI: 10.1049/PBPO145E
- ISBN: 9781785618659
- e-ISBN: 9781785618666
- Page count: 434
- Format: PDF
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Front Matter
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1 Introduction to condition monitoring
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This book is about the condition monitoring (CM) of rotating electrical machines. To develop this understanding, we need to acknowledge: fundamental electrical machines principles, principles of variable speed drives applied to electrical machines, principles of reliability applied to machines and drives so that we can understand how CM can best be applied.
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2 Rotating electrical machines
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There are currently an enormous range of VSDs being applied to electrical machines and there are a number of specialised references available for considering VSD reliability, see Appendix C -Timeline, including Yang and Ran et al. (2010) and Chung et al. (2016). A great deal of this development is being driven by the electrification of transport, on the railway and the roads. For example, Figure 2.17 shows an example of a high-performance electric car permanent magnet motor and drive. The conclusion of this chapter is that all electrical machines are remarkably similar in their logic and design, despite there being many different types and progressive changes in their application. However, they are all dominated by the coupling magnetic field, which is their modus operandi. It follows therefore that their construction, failure modes and CM must derive from that fundamental mechanism, the materials which facilitate it and their operating conditions, as the following chapter will demonstrate. We will need to show how CM can take these facts into account.
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3 Electrical machine construction, operation and failure modes
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This chapter could be subtitled 'the way rotating electrical machines fail in service'. These machines convert electrical to mechanical energy, or vice versa, and achieve this by magnetically coupling electrical circuits across an air-gap that permits rotational freedom of one of these circuits. Mechanical energy is transmitted into, or out of, the machine via a drive train that is mechanically connected to one of the electric circuits. The purpose of this chapter is to explain their constructional principles and the main causes of failure. The chapter is illustrated with a number of photographs to demonstrate to the reader the salient features of electrical machines.
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4 Reliability of machines and typical failure rates
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This chapter has shown that by addressing failure modes that are slow to mature, CM could significantly affect the detection of faults before they occur. CM can then form part of planning fixed-time interval maintenance (ii) and will be at the heart of maintenance for method (iii). Prior to determining the type of equipment and frequency of monitoring, some consideration should also be given to whether the cost of implementing such a program is justified. Factors involved in this decision may include: Replacement or repair cost of the equipment to be monitored; Criticality of the plant to safe and reliable operation; Long term future of the facility in which the equipment is installed.
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5 Signal processing and instrumentation requirements
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This chapter has described the signal-processing methods now available for computer monitoring electrical machines and the wide range of instrumentation and processing techniques used to monitor machines. Modern techniques and methods have been shown and the next chapters show how the signals from these have been applied to provide CM information. The following chapters will therefore describe the major temperature, chemical, mechanical and electrical techniques available for monitoring.
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6 Online temperature monitoring
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This chapter shows that temperature measurement yields valuable bulk indications of the condition of an electrical machine using simple sensors and narrow bandwidth (<1 Hz), low data rate signals and, because temperature limits the rating of a machine, overtemperature is a valuable CM signal. Temperature detection has repeatedly been shown to be an effective global monitoring technique for electrical machines, but has been neglected as a monitoring method. An important conclusion of this work is that the availability of new FBG temperature sensors gives the potential for much greater distributed electrical machine temperature measurements.
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7 Online chemical monitoring
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This chapter has shown that chemical and wear analysis have been demonstrated to be effective global monitoring techniques for electrical machines which can produce narrow bandwidth (<1 Hz) signals. Chemical degradation of insulating materials and lubricants is detectable and can give bulk indications of the condition of an electrical machine. This is particularly important when it is considered how central insulation and lubrication integrity are to the long-term life of a machine.
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8 Online vibration monitoring
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An electrical machine, its support structure and the load to which it is coupled, form a complex electromechanical system. It can receive impulsive excitations that vibrates it at its own natural frequency, or it can be forced, by the exciting air -gap electromagnetic fi eld or torque spectrum of the driven or driving machine, at many different frequencies. These frequencies may cause the machine to emit an unacceptably high level of acoustic noise, or cause progressive mechanical damage, due to high cycle fatigue, which ends in a machine failure mode.
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9 Online current, flux and power monitoring
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This chapter has shown that electrical techniques are powerful tools for the CM of electrical machines, particularly axial leakage flux, current and power, offering the potential to provide a general CM signal for the machine. The availability of high -quality, digitally sampled, mechanical vibration and electrical terminal 'performance' data from electrical machines opens the possibility for more comprehensive monitoring of the machine and prime mover or driven machine combinations. However, these signals generally require broad bandwidth (>50 kHz) and a high data rate for adequate analysis. Therefore, the principal difficulty of applying these techniques is the complexity of the necessary spectral analysis and interpretation of their content.
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10 Online partial discharge (PD) electrical monitoring
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Discharge measurement has shown itself to be the most problematic electrical method of electrical machine CM. It requires special sensors, wide bandwidth (>100 kHz) and very complex analysis for fault detection. It can only be recommended where a specific and costly high-voltage failure mode is being searched for in a known location on a large machine. It addresses one of the most vital parts of the electrical machine, it can detect global effects, including possibly remanent life of the machine insulation and it does give a long warning before failure occurs. Yet, the analysis of the previous chapter shows that, with modern materials, the proportion of machine failures due to insulation faults are now less than a third. Furthermore, the detection methods rely on the most advanced signal processing to extract useful indications, which are then open to wide interpretation by PD measurement experts. This has made it extremely difficult to increase the confidence of machine operators in the value of this type of monitoring because of their need to refer to differing expert opinion. PD monitoring was first applied to isolated insulated components, such as bushings and cable stop joints where it had and continues to have a vital role to play. Its greatest impact to date has been on transformers, substation plant, gas and air-insulated switchgear, where specific failure modes in particular locations are being searched for using both wide-band RFI and narrow-band EMI techniques, with support from acoustic measurement. However, when applied to the distributed, multi-path, multi-connection, variably stressed insulation system of an electrical machine winding, it has a much more difficult task. These methods have been valuable on large machines such as hydro-generators where stator winding fault locations are limited to particular machine ends and their high-voltage failure mode connections, allowing the precise location of sensor couplers, the tailoring of signal processing to that failure mode and where the asset value justifies the application of complex techniques. Work still continues to develop this method, including the use of AI, in these chapter, to determine the overall deterioration of a winding insulation system but that objective has not yet been reached. However, the most thorough treatise to navigate this complex but promising technology.
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11 Online variable speed drive machine monitoring
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Electrical machines in VSDs have been reported to be less reliable than those fed direct -on-line from the grid mains, see Montanan. This is because the voltage supply imposes extra stresses when synthesised by a power electronic converter. Our focus here is on drive systems using voltage source converters (VSC) where the DC link is capacitively smoothed. Two-level pulse-width-modulation (PWM) is usually used in low-voltage drives for machine voltages up to 690 V,line-line. Multi-level converters may be used in MV drives for motors up to 11 kV, see Stone et al. (2014). In addition to voltage source converters, there are also current source converters with an inductively smoothed DC link for large motors rated at tens of MW. Harmonics and inter-harmonics in the inverter output and machine windings also increase machine stresses through electrical, thermal and mechanical effects. we will outline some major and common issues of electrical machines in VSDs or generator systems and describe the available techniques for monitoring the major fault mechanisms which are new to VSDs. The working principles of these techniques will be analysed to understand their capabilities and limitations.
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12 Offline monitoring
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Offline testing for electrical machines has developed considerably over the last 30 years and this is represented in the summary tables above but also in the availability of a number of internationally recognised standards. But a review of the tables shows that the most developed off-line techniques are associated with the stator cores and windings and rotor windings of larger turbo-generators and hydro-generators. Not all offline tests are easy to perform and many cannot duplicate operational conditions. In many ways, the authors suggest that offline testing, particularly for larger machines, provides the background diagnostic information which should be used to direct the focus of subsequent online CM.
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13 Condition-based maintenance and asset management
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This chapter describes how CBM, life-cycle costing and asset management could be applied to rotating electrical machines. The chapter includes examples of electrical machines in context, particularly conventional power generation and wind energy. However, further benefits can be realised if decisions on maintenance scheduling are taken to reduce total life-cycle costs of the whole machine and plant it serves. This requires an estimate of the variable costs of operating and maintaining the plant throughout its life and, increasingly, reliable performance measures, such as energy production, against which to evaluate costs. With this knowledge, the plant or asset owner can operate, maintain, or dispose of that asset on the basis of the information available from these processes.
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14 Application of artificial intelligence techniques to CM
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CM has to establish a map between the input signals and output indications of the machine condition. Classifying the machine condition and determining the severity of faults from the input signals have never been easy tasks and they are affected by several factors.
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15 Safety, training and qualification
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This book is mainly concerned the science and technology of electrical machine CM. However, since our first edition, this has now become a significant industry in support of our use of electrical machinery for generation and general plant. As the appendices show there are now international standards applicable to CM of electrical machinery, although these are still in early stages of development. Consequently, industry is demanding a level of expertise from its CM operatives commensurate with the importance of the task in hand. Therefore, this final chapter briefly summarises the safety, training and qualifications that should be expected of staff undertaking CM. They may not need to be experts in electrical machinery but must be safe and competent in the operation of monitoring equipment and in the recording and accurate presentation of the information that arises from it, if their work is to make a sound contribution to condition-based maintenance and asset management.
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16 Overall conclusions
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This book has described the following major monitoring techniques for electrical machines, in the following ascending order of usefulness: Shaft voltage or current has been suggested as universal panaceas for comprehensive electrical machine monitoring but have not been proven as reliable CM techniques. Shaft flux, current and power signals are capable of detecting faults in both the electrical and mechanical parts of a drive train. Shaft flux monitoring is non-invasive, broad bandwidth (5 Hz-20 kHz) and uses a single flux coil sensor, but it is complex to analyse and untested in the field. Little further work has been done on this technology since its inception in the 1980s, therefore it cannot be recommended to operators. However, some new industrial applications, such as traction, electric cars or aerospace, could make it exceptionally appealing because of its ease of fitting and universality. Motor speed has been analysed to detect rotor electrical faults, IAS, it is simple and requires a low bandwidth signal (<1 kHz) but has not been widely used by operators. Vibration and shock pulse monitoring and analysis are non-invasive but use widely available sensors and broad bandwidth with complex analysis (5 Hz-50 kHz). The precise selection and location of these sensors is very important as they are generally distant from the seat of any defect action and signals maybe attenuated by machine and bearing enclosure responses.
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References
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Appendix A. Failure modes and root causes in the rotating electrical machines
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Appendix B. Draft CM good practice guide, MCSA
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Motor current signature analysis (MCSA) is an electrical condition-monitoring technique for detecting electrical and mechanical defects in rotating machines by making accurate measurements of the stator current and identifying and analysing the frequency components within this current. The air-gap of an electrical machine is the clearance between a rotating rotor and the fixed stator core. The magnetic field of the machine crosses this air-gap and modulates the stator current, whether it is a motor or generator. Any defect in the machine, which affects that air-gap magnetic field, will modulate the machine current signature and can be detected by measurement of those modulations.
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Appendix C. Electrical machines, drives and condition monitoring timeline
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Back Matter
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