Electrical Systems 2 E-Book

• Herausgeber: John Wiley & Sons
• Kategorie: Wissenschaft und neue Technologien
• Sprache: Englisch

Seitenzahl: 278

Veröffentlichungsjahr: 2020

Beschreibung

Methods of diagnosis and prognosis play a key role in the reliability and safety of industrial systems. Failure diagnosis requires the use of suitable sensors, which provide signals that are processed to monitor features (health indicators) for defects. These features are required to distinguish between operating states, in order to inform the operator of the severity level, or even the type, of a failure. Prognosis is defined as the estimation of a system�s lifespan, including how long remains and how long has passed. It also encompasses the prediction of impending failures. This is a challenge that many researchers are currently trying to address. Electrical Systems, a book in two volumes, informs readers of the theoretical solutions to this problem, and the results obtained in several laboratories in France, Spain and further afield. To this end, many researchers from the scientific community have contributed to this book to share their research results.

Leseprobe

Table of Contents

Cover

Introduction

1 Diagnosis of Electrical Machines by External Field Measurement

1.1. Introduction

1.2. Extracting indicators from the external magnetic field

1.3. Information fusion to detect the inter-turn short-circuit faults

1.4. Application

1.5. Conclusion

1.6. References

2 Signal Processing Techniques for Transient Fault Diagnosis

2.1. Introduction

2.2. Fault detection via motor current analysis

2.3. Signal processing tools for transient analysis

2.4. Application of transient-based tools for electric motor fault detection

2.5. Conclusions

2.6. References

3 Accurate Stator Fault Detection in an Induction Motor Using the Symmetrical Current Components

3.1. Introduction

3.2. Study of the SCCs behavior in an IM under different stator faults

3.3. Extracting stator fault indicators from an IM

3.4. Automatic and accurate detection and diagnosis of stator faults

3.5. Conclusion

3.6. References

4 Bearing Fault Diagnosis in Rotating Machines

4.1. Introduction

4.2. Method description

4.3. Experimental data

4.4. Global spectra bearing diagnosis

4.5. Conclusion

4.6. References

5 Diagnosis and Prognosis of Proton Exchange Membrane Fuel Cells

5.1. Introduction

5.2. PEMFC functioning principle and development status

5.3. Faults and degradation of PEMFCs

5.4. PEMFC diagnostic methods

5.5. Prognosis of PEMFCs

5.6. Remaining challenges

5.7. References

List of Authors

Index

Summary of Volume 1

End User License Agreement

List of Tables

Chapter 1

Table 1.1. Conjunctive combination of MFs m

1

and m

2

Table 1.2. Measurements obtained by sensors C1 and C2 using the induction machin...

Table 1.3. DV obtained from sensors C1 and C2 using the induction machine for po...

Table 1.4. Number of different variations detected for each position of the sens...

Table 1.5. MFs m

DV,i

obtained from the measurement exposed in Table 1.2

Table 1.6. MFs m

DV,i

obtained from the measurement exposed in Table 1.2

Table 1.7. MFs m

RA,i

from the measurement exposed in Table 1.2

Table 1.8. Characteristics of the tested machines

Table 1.9. Percent of correct decisions obtained, for the AM in the case of diff...

Table 1.10. Percent of correct decisions obtained for the AM in the case of diff...

Table 1.11. Percent of correct decisions obtained for the SM in the case of diff...

Chapter 2

Table 2.1. Groups of t–f tools

Table 2.2. Frequency bands associated with wavelet signals for fs=5 kHz and n=8

Chapter 3

Table 3.1. Characteristics of the simulated 1.1-kW IM

Table 3.2. Magnitudes and phase angles of the NSC and ZSC for different ITSC fau...

Table 3.3. Magnitudes in (A) of the NSC for different faults between phases A an...

Table 3.4. Phase angles φ

2

in (°) of the NSC for different faults between phases...

Table 3.5. Phase angles φ

2

in (°) of the NSC for different faults between phases...

Table 3.6. Phase angles φ

2

in (°) of the NSC for different faults between phases...

Table 3.7. Magnitudes and phase angles of the NSC and ZSC for different phase-to...

Table 3.8. Analytical expressions of the magnitudes and phase angles of the NSC ...

Table 3.9. Analytical expressions of the magnitudes and phase angles of the NSC ...

Table 3.10. Analytical expressions of the magnitudes and phase angles of the NSC...

Table 3.11. Comparison between the measured and calculated values of the NSC and...

Table 3.13. Comparison between the measured and calculated values of the NSC and...

Table 3.14. Value of the magnitudes and phase angles of the different components...

Table 3.15. Electrical parameters of the (2-s) model of the IM used

Table 3.16. Comparison between the NSC with and without compensation in the case...

Table 3.17. Values of and of an IM supplied by imbalanced voltage U

v-b

and u...

Table 3.18. Values of and of an IM supplied by imbalanced voltage U

v-bc

and ...

Table 3.19. Values of and of an IM with the fault F

2-bc

and under different ...

Chapter 4

Table 4.1. Confusion matrix (PCA space)

Table 4.2. Confusion matrix (LDA space) (Harmouche et al. 2015)

Chapter 5

Table 5.1. PEMFC durability status of 2018 versus target 2020 [SAU 18]

Table 5.2. Variables can be used for the diagnosis and prognosis of PEMFCs

Table 5.3. Configuration of λ

air

at I

stk

= 30 A

Table 5.4. Fuzzy rule set (NEXA™ data)

Table 5.5. Variables used for the diagnosis and prognosis of PEMFCs

Table 5.6. Variables used for the diagnosis and prognosis of PEMFCs

Guide

Cover

Table of Contents

Begin Reading

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Electrical Systems 2

From Diagnosis to Prognosis

Edited by

Abdenour Soualhi

Hubert Razik

First published 2020 in Great Britain and the United States by ISTE Ltd and John Wiley & Sons, Inc.

Apart from any fair dealing for the purposes of research or private study, or criticism or review, as permitted under the Copyright, Designs and Patents Act 1988, this publication may only be reproduced, stored or transmitted, in any form or by any means, with the prior permission in writing of the publishers, or in the case of reprographic reproduction in accordance with the terms and licenses issued by the CLA. Enquiries concerning reproduction outside these terms should be sent to the publishers at the undermentioned address:

ISTE Ltd27-37 St George’s RoadLondon SW19 4EUUK

www.iste.co.uk

John Wiley & Sons, Inc.111 River StreetHoboken, NJ 07030USA

www.wiley.com

© ISTE Ltd 2020

The rights of Abdenour Soualhi and Hubert Razik to be identified as the authors of this work have been asserted by them in accordance with the Copyright, Designs and Patents Act 1988.

Library of Congress Control Number: 2019956924

British Library Cataloguing-in-Publication Data

A CIP record for this book is available from the British Library

ISBN 978-1-78630-608-1

Introduction

The diagnosis and prognosis of electrical systems is still a relevant field of research. The research that has been carried out over the years has made it possible to acquire enough knowledge, to build a base from which we can delve further into this field of research. This study is a new challenge that estimates the remaining lifetime of the analyzed process. Many studies have been carried out to establish a diagnosis of the state of health of an electric motor, for example. However, making a diagnosis is like giving binary information: the condition is either healthy or defective. Of course, this may seem simplistic, but detecting a failure requires the use of suitable sensors that provide signals. These will be processed to monitor health indicators (features) for defects. Then, we witnessed a multitude of research activities around classification. It was indeed appropriate to distinguish the operating states, to differentiate them from one another and to inform the operator of the level of severity of a failure or even of the type of failure among a predefined panel. A major effort has been made to estimate the remaining lifetime or even the lifetime consumed. This is a challenge that many researchers are still trying to meet.

This book, which has been divided into two volumes, informs readers about the theoretical approaches and results obtained in different laboratories in France and also in other countries such as Spain, and so on. To this end, many researchers from the scientific community have contributed to this book by sharing their research results.

Chapter 1, Volume 1, “Diagnostic Methods for the Health Monitoring of Gearboxes”, by A. Soualhi and H. Razik, presents state-of-the-art diagnostic methods used to analyze the defects present in gearboxes. First of all, there is a bibliographical presentation regarding different types of gears and their defects. We conclude that gear defects represent the predominant defect at this level, thus justifying the interest in detecting and diagnosing them. Then, we present various gear analyses and monitoring techniques proposed as part of the condition-based maintenance and propose a diagnostic method. Thus, we show the three main phases of diagnosis: First, the analysis presented as a set of technical processes ensuring control of the representative quantities of operation; then the monitoring that exploits the fault indicators for detection; finally, the diagnosis which is the identification of the detected defect.

Chapter 2, Volume 1, “Techniques for Predicting Defects in Bearings and Gears”, by A. Soualhi and H. Razik, deals with strategies based on features characterizing the health status of the system to predict the appearance of possible failures. The prognosis of faults in a system means the prediction of the failure imminence and/or the estimation of its remaining life. It is in this context that we propose, in this chapter, the three methods of prognosis. In the first method, the degradation process of each system is modeled by a hidden Markov model (HMM). In a measured sequence of observations, the solution consists of identifying among the HMMs the one that best represents this sequence which allows predicting the imminence of the next degradation state and thus the defect of the studied system. In the second method (evolutionary Markov model), the computation of the probability that a sequence of observations arrives at a degradation state at the moment t+1, given the HMM modeled from the same sequence of observations, also allows us to predict the imminence of a defect. The third method predicts the imminence of a fault not by modeling the degradation process of the system, but by modeling each degradation state.

Chapter 3, Volume 1, “Electrical Signatures Analysis for Condition Monitoring of Gears in Complex Electromechanical Systems,” written by S. Hedayati Kia and M. Hoseintabar Marzebali, deals with a review of their most remarkable research, which has been carried out in the last 10 years. A particular emphasis has been placed on the topic of noninvasive fault detection in gears using electrical signatures analysis. The main aim is to utilize the electrical machine as a sensor for the identification of gear defects. In this regard, a universal approach is developed for the first time by the authors which allows evaluating the efficacy of noninvasive techniques in the diagnosis of torsional vibration induced by the faulty gear located within the drive train. This technique can be considered an upstream phase for studying the feasibility of gear fault detection using noninvasive measurement in any complex electromechanical system.

Chapter 4, Volume 1, “Modal Decomposition for Bearing Fault Detection”, by Y. Amirat, Z. Elbouchikri, C. Delpha, M. Benbouzid and D. Diallo, deals with induction machine bearing faults detection based on modal decomposition approaches combined to a statistical tool. In particular, a comparative study of a notch filter based on modal decomposition, through an ensemble empirical mode decomposition and a variational mode decomposition, is proposed. The validation of these two approaches is based on simulations and experiments. The achieved simulation and experimental results clearly show that, in terms of fault detection criterion, the variational mode decomposition outperforms the ensemble empirical mode decomposition.

Chapter 5, Volume 1, “Methods for Lifespan Modeling in Electrical Engineering”, by A. Picot, M. Chabert and P. Maussion, deals with the statistical methods for electrical device lifespan modeling from small-sized training sets. Reliability has become an important issue in electrical engineering because the most critical industries, such as urban transports, energy, aeronautics or space, are moving toward more electrical-based systems to replace mechanical- and pneumatic-based ones. In this framework, increasing constraints such as voltage and operating frequencies enhance the risk of degradation, particularly due to partial discharges (PDs) in the electrical machine insulation systems. This chapter focuses on different methods to model the lifespan of electrical devices under accelerated stresses. First, parametric methods such as design of experiments (DoE) and surface responses (SR) are suggested. Although these methods require different experiments to organize in a certain way, they reduce the experimental cost. In the case of nonorganized experiments, multilinear regression can help estimate the lifespan. In the second part, the nonparametric regression tree method is presented and discussed, resulting in the proposal of a new hybrid methodology that takes advantages of both parametric and nonparametric modeling. For illustration purpose, these different methods are evaluated on experimental data from insulation materials and organic light-emitting diodes.

Chapter 1, Volume 2, “Diagnosis of Electrical Machines by External Field Measurement”, by R. Pusca, E. Lefevre, D. Mercier, R. Romary and M. Irhoumah, presents a diagnostic method that exploits the information delivered by external flux sensors placed in the vicinity of rotating electrical machines in order to detect a stator inter-turn short circuit. The external magnetic field measured by the flux sensors originates from the airgap flux density and from the end winding currents, attenuated by the magnetic parts of the machine. In the faulty case, an internal magnetic dissymmetry occurs, which can be found again in the external magnetic field. Sensitive harmonics are extracted from the signals delivered by a pair of flux sensors placed at 180° from each other around the machine, and the data obtained for several sensor positions are analyzed by fusion techniques using the belief function theory. The diagnosis method is applied on induction and synchronous machines with artificial stator faults. It is shown that the probability of detecting the fault using the proposed fusion technique on various series of measurements is high.

Chapter 2, Volume 2, “Signal Processing Techniques for Transient Fault Diagnosis”, by J.A. Daviu and R.A.O. Rios, revises the most relevant signal processing tools employed for condition monitoring of electric motors. First, the importance of the predictive maintenance area of the electric motors due to the extensive use of these machines in many industrial applications is pointed out. In this context, the most important predictive maintenance techniques are revised, showing the advantages such as the simplicity, remote monitoring capability and broad fault coverage of motor current analysis methods. In this regard, two basic approaches based on current analysis are explained: the classical methods, relying on the Fourier transform of steady-state current (motor current signature analysis – MCSA), and novel methods based on the analysis of startup currents (advanced transient current signature analysis – ATCSA). In the chapter, the most significant signal processing tools employed for MCSA and ATCSA are explained and revised. For MCSA, the basic problems derived from the application of the Fourier transform as well as other constraints of the methodology are explained. For ATCSA, the most suitable signal processing techniques are described, classifying them into continuous and discrete transforms. One representative of each group is accurately described (the discrete wavelet transform for discrete tools and the Hilbert-Huang transform for continuous tools), accompanying the explanation with illustrative examples. Finally, we discussed several examples of the application of each tool to electric motor fault diagnosis.

Chapter 3, Volume 2, “Accurate Stator Fault Detection in an Induction Motor Using the Symmetrical Current Components”, by M. Bouzid and G. Champenois, deals with the accurate detection of stator faults such as inter-turns short circuit, phase-to-phase and phase-to-ground faults of the induction motor, using the symmetrical current components. The detection method is based on the monitoring of the behavior of the negative and zero sequence stator currents of the machine. This chapter also develops analytical expressions of these components obtained using the coupled inductance model of the machine. However, despite its efficiency, the negative sequence current-based method has its own limitations to detect accurate incipient stator faults in an induction motor. This limit can be explained by the fact that the negative sequence current generated in a faulty motor does represent not only the asymmetry introduced by the fault, but also by other superposed asymmetries, such as the voltage imbalance, the inherent asymmetry in the machine and the inaccuracy of the sensors. This aspect can generate false alarm and make the achievement of accurate incipient stator fault detection very difficult. Thus, to increase the accuracy of the fault detection and the sensitivity of the negative sequence current under different disturbances, this chapter proposes an efficient method able to compensate the effect of the different considered disturbances using experimental techniques having the originality to isolate the negative sequence current of each disturbance. The efficiency of all these proposed methods is validated experimentally on a 1.1-kW motor under different stator faults. Moreover, an original monitoring system, based on neural networks, is also presented and described to automatically detect and diagnose incipient stator faults.

Chapter 4, Volume 2, “Bearing Fault Diagnosis in Rotating Machines”, by C. Delpha, D. Diallo, J. Harmouche, M. Benbouzid, Y. Amirat and E. Elbouchikhi, is focused on detection, estimation and diagnosis of mechanical faults in electrical machines. Nowadays, it is necessary to rapidly assess the structural health of a system without disassembling its elements. For this in situ diagnosis purpose, the use of experimental data is very imperative. Moreover, the monitoring and maintenance costs must be reduced while ensuring satisfactory security performances. In this chapter, we focus on vibration-based signals combined with statistical techniques for bearing fault evaluation. Based on a four-step diagnosis process (modeling, preprocessing, feature extraction and feature analysis), the combination of several techniques such as principal components analysis and linear discriminant analysis in a global approach is explored to monitor the condition of vibration-based bearings. The main advantage of this approach is that prior knowledge on the bearing characteristics is not required. A particularly reduced frequency analysis has led to efficiently differentiate the bearing fault types and evaluate the bearing fault severities.

Chapter 5, Volume 2, “Diagnosis and Prognosis of Proton Exchange Membrane Fuel Cells”, by Z. Li, Z. Zheng and F. Gao, deals with the diagnostic and prognostic issues of fuel cell systems, especially the proton exchange membrane (PEMFC) type. First, the basic functioning principle of PEMFCs and their current development and application status are presented. Their high cost, low reliability and durability make them unfit for commercialization. In the following sections, degradation mechanisms related to both the aging effect and the system operations are analyzed. In addition, typical variables and characterization tools, such as polarization curve, electrochemical impedance spectroscopy, linear sweep voltammetry and cyclic voltammetry, are introduced for the evaluation of PEMFC degradation. Various diagnostic and prognostic methods in the literature are further classified based on their input-to-output process model of the system, namely model-based, data-driven and hybrid methods. Finally, two case studies for diagnosis and prognosis are given at the end of each part to give the readers a general and clearer illustration of these two issues.

Introduction written by Abdenour SOUALHI and Hubert RAZIK.

1Diagnosis of Electrical Machines by External Field Measurement

1.1. Introduction

Rotating electrical machines are found in all areas of modern domestic and industrial life [TAV 08]. They are the main electromechanical energy conversion devices in all industrial processes and have been widely used in different industrial applications for several decades. They account for approximately 70% of all electricity consumed on the grid and 80% of industrial engines involved in manufacturing processes. Regardless of the size of these units, from 1 kilowatt to several megawatts, the production losses due to a shutdown relating to an engine failure are greater than those induced by the actual engine efficiency. The failure of the machines, therefore, reduces the production rate and increases production and maintenance costs. It is then important to reduce maintenance costs and avoid unplanned downtime for these machines. Electrical machines must be monitored during the production process to improve their reliability and reduce their downtime [STO 04, ESE 17, NOR 93]. Monitoring of rotating electrical machines is still an essential part to increase reliability and operational safety of electrical systems and has been the subject of much research in recent decades [STO 04, HAN 10, PET 17].

Electric motors encounter a wide range of mechanical problems common to most machines, such as imbalance, misalignment, bearing faults and resonance [FOU 15, HAM 15, KAT 16]. But electric motors also encounter their specific problems, which are the result of electromagnetic phenomena. The methods conventionally used for the diagnosis of electrical machines are based on measurements of current, voltage, vibration and noise. Although their effectiveness has been demonstrated, the generalization of these methods in the industrial environment remains limited on account of their relatively important cost.

Other methods based on magnetic field measurements outside the machine are interesting because they are inexpensive and easy to implement. Thus, monitoring devices based on the information provided by the magnetic flux produced by the imbalances in the magnetic or electrical circuit of the motors can be effectively used in addition to, or as an alternative to the current monitoring more conventionally used. Thus, many recent methods, used for the diagnosis of electrical machines, are based on the analysis of combining measurements of current and magnetic flux, where, on the basis of an evaluation of many tests, the stator current and the external leakage flux were selected as the most practical signals containing the information needed to detect broken bars and short circuit between turns of the stator winding [CEB 12a, YAZ 10].

The methods presented in this chapter propose solutions to improve the detection of stator inter-turn short-circuit fault by external field analysis [CEB 12b]. For this, it uses the processing of data obtained by several field sensors and fusion methods suitable for applications in signal processing. In this area, the information fusion must take into account the specificities of the data in considered process [DAS 01]. In our case, information fusion tools use the belief function theory [SHA 76, PUS 12, IRH 18]. This theory is a mathematical framework that offers modeling and fusion tools, and it also enables a relatively natural integration of the data imperfections in the analysis. For implementation of the proposed method, the measurements of the external magnetic field are exploited in order to construct two specific pieces of information: the difference of variation and the ratio of the amplitudes. In order to make a more relevant decision, a fusion process is applied to merge these two pieces of information by transforming them into belief functions. After their fusion, a decision can be made.

The method proposed in this chapter is fully noninvasive and can be implemented for asynchronous (AM) and synchronous machines (SM). Its main advantage is that it does not require the knowledge of the healthy state of the machine. In the analysis, it exploits the load variation of sensitive spectral lines instead of their magnitude. The sensitive lines are chosen considering the AM or SM specificity as presented in the following section.

1.2. Extracting indicators from the external magnetic field

One of the main issues for exploiting the external magnetic field is to define reliable indicators from it. This requires a good knowledge of the electromagnetic behavior of the machine in the faulty condition. Here, we present an analytical modeling of an electrical machine with a stator inter-turn short circuit fault, associated with a simplified decomposition of the external magnetic field.

1.2.1. External field classification

From a physical point of view, an external magnetic field appears in the vicinity of an electrical machine because the internal magnetic field is not perfectly channeled by the ferromagnetic parts of the machine. This external magnetic field can be decomposed in transverse and axial components. The axial field is in a plane that contains the machine axis. It is generated by the winding overhang effects. The transverse field is located in a perpendicular plane to the machine axis. It is an image of the airgap flux density b which is attenuated by the stator magnetic circuit. Figure 1.1 shows a simplified representation of both fields.