Electrical Systems 2

From Diagnosis to Prognosis

Inbunden, Engelska, 2020

AvAbdenour Soualhi,Hubert Razik,France) Soualhi, Abdenour (Jean Monnet University,France) Razik, Hubert (University Lyon I

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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.

Produktinformation

Utgivningsdatum2020-03-13
Mått160 x 234 x 15 mm
Vikt454 g
FormatInbunden
SpråkEngelska
Antal sidor224
FörlagISTE Ltd and John Wiley & Sons Inc
ISBN9781786306081

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Abdenour Soualhi is an Assistant Professor in Electrical and Mechanical Engineering at LASPI Laboratory, Jean Monnet University, Roanne, France. His main research interests are in signal processing, diagnosis and prognosis of faults. Hubert Razik is a Full Professor of Electrical Engineering at Claude Bernard University Lyon I, France. His main research interests include modeling, control and monitoring conditions of multiphase induction motors.

Introduction ixChapter 1. Diagnosis of Electrical Machines by External Field Measurement 1Remus PUSCA, Eric LEFEVRE, David MERCIER, Raphael ROMARY and Miftah IRHOUMAH1.1. Introduction 11.2. Extracting indicators from the external magnetic field 31.2.1. External field classification 31.2.2. Attenuation of the transverse field 51.2.3. Measurement of the transverse field 61.2.4. Modeling a healthy machine 81.2.5. Modeling a faulty machine 101.2.6. Effect of the load 131.3. Information fusion to detect the inter-turn short-circuit faults 161.3.1. Belief function theory: basic concepts 171.3.2. Fault detection with the fusion method 191.3.3. Calculation example 211.4. Application 251.4.1. Presentation of rotating electrical machines 251.4.2. Presentation of experimental results 281.5. Conclusion 331.6. References 33Chapter 2. Signal Processing Techniques for Transient Fault Diagnosis 37José Alfonso Antonino DAVIU and Roque Alfredo Osornio RIOS2.1. Introduction 372.2. Fault detection via motor current analysis 412.2.1. Classical tools (MCSA) 412.2.2. New techniques based on transient analysis (ATCSA) 452.3. Signal processing tools for transient analysis 472.3.1. Example of a discrete tool: the DWT 482.3.2. Example of a continuous tool: the HHT 542.4. Application of transient-based tools for electric motor fault detection 672.4.1. Application of the DWT for the detection of rotor damage 682.4.2. Application of the HHT for the detection of rotor damage 702.5. Conclusions 712.6. References 72Chapter 3. Accurate Stator Fault Detection in an Induction Motor Using the Symmetrical Current Components 77Monia BOUZID and Gérard CHAMPENOIS3.1. Introduction 773.2. Study of the SCCs behavior in an IM under different stator faults 793.2.1. Simulation study 793.2.2. Analytical study of the SCCs in an IM under different stator faults 863.3. Extracting stator fault indicators from an IM 973.4. Automatic and accurate detection and diagnosis of stator faults 983.4.1. Description of the monitoring system of the IM operating state 983.4.2. Improving the accuracy of incipient stator fault detection 993.4.3. Automatic incipient stator fault diagnosis in an IM 1143.5. Conclusion 1183.6. References 119Chapter 4. Bearing Fault Diagnosis in Rotating Machines 123Claude DELPHA, Demba DIALLO, Jinane HARMOUCHE, Mohamed BENBOUZID, Yassine AMIRAT and Elhoussin ELBOUCHIKHI4.1. Introduction 1244.1.1. Bearing fault detection and diagnosis overview 1244.1.2. Problem statement and proposal 1284.2. Method description 1304.2.1. The global spectral analysis description 1304.2.2. Discrimination of faults in the bearing balls using LDA 1334.3. Experimental data 1354.3.1. Experimental test bed description 1354.3.2. Time-domain detection 1374.4. Global spectra bearing diagnosis 1394.4.1. Data preprocessing 1394.4.2. Global spectra results with PCA 1414.4.3. Global spectra results with LDA 1434.5. Conclusion 1464.6. References 147Chapter 5. Diagnosis and Prognosis of Proton Exchange Membrane Fuel Cells 153Zhongliang LI, Zhixue ZHENG and Fei GAO5.1. Introduction 1535.2. PEMFC functioning principle and development status 1545.2.1. From a PEMFC to a PEMFC system 1545.2.2. Current status of the PEMFC technology 1565.3. Faults and degradation of PEMFCs 1575.3.1. Degradation related to the aging effects 1575.3.2. Degradation related to system operations 1585.3.3. Variables used for PEMFC degradation evaluation 1615.4. PEMFC diagnostic methods 1655.4.1. Model-based diagnostic methods 1655.4.2. Data-driven diagnostic methods 1685.4.3. Case study 1715.5. Prognosis of PEMFCs 1805.5.1. Health index and EoL 1815.5.2. Model-based prognostic methods 1825.5.3. Data-driven and hybrid prognostic methods 1845.5.4. Case study 1865.6. Remaining challenges 1935.7. References 194List of Authors 199Index 201Summary of Volume 1 203