Fault Diagnosis and Prognosis Techniques for Complex Engineering Systems gives a systematic description of the many facets of envisaging, designing, implementing, and experimentally exploring emerging trends in fault diagnosis and failure prognosis in mechanical, electrical, hydraulic and biomedical systems. The book is devoted to the development of mathematical methodologies for fault diagnosis and isolation, fault tolerant control, and failure prognosis problems of engineering systems. Sections present new techniques in reliability modeling, reliability analysis, reliability design, fault and failure detection, signal processing, and fault tolerant control of engineering systems.
Sections focus on the development of mathematical methodologies for diagnosis and prognosis of faults or failures, providing a unified platform for understanding and applicability of advanced diagnosis and prognosis methodologies for improving reliability purposes in both theory and practice, such as vehicles, manufacturing systems, circuits, flights, biomedical systems. This book will be a valuable resource for different groups of readers mechanical engineers working on vehicle systems, electrical engineers working on rotary machinery systems, control engineers working on fault detection systems, mathematicians and physician working on complex dynamics, and many more.
- Presents recent advances of theory, technological aspects, and applications of advanced diagnosis and prognosis methodologies in engineering applications
- Provides a series of the latest results, including fault detection, isolation, fault tolerant control, failure prognosis of components, and more
- Gives numerical and simulation results in each chapter to reflect engineering practices
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ix | |
| Preface |
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xi | |
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1 Quality-related fault detection and diagnosis: a technical review and summary |
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1 | (5) |
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6 | (3) |
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9 | (18) |
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27 | (16) |
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Appendix A Description of the variables and faults |
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43 | (4) |
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47 | (4) |
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2 Canonical correlation analysis-based fault diagnosis method for dynamic processes |
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51 | (2) |
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53 | (10) |
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2.3 CCA-based fault diagnosis method for dynamic processes |
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63 | (8) |
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2.4 Experimental results and analysis |
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71 | (11) |
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82 | (2) |
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84 | (1) |
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84 | (5) |
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3 Hoc Fault estimation for linear discrete time-varying systems with random uncertainties |
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89 | (2) |
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3.2 Robust H∞ fault detection for LDTV systems with multiplicative noise |
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91 | (11) |
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3.3 Robust H∞ fault detection for LDTV systems with measurement packet loss |
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102 | (9) |
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3.4 Fixed-lag H∞ fault estimator design for LDTV systems under an unreliable communication link |
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111 | (12) |
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123 | (1) |
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123 | (1) |
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123 | (4) |
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4 Fault diagnosis and failure prognosis of electrical drives |
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127 | (5) |
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4.2 What can fail and how |
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132 | (12) |
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4.3 Diagnosis methodology and tools |
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144 | (6) |
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4.4 Faults, their manifestation, and diagnosis |
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150 | (15) |
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4.5 Failure prognosis, fault mitigation, and reliability |
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165 | (10) |
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175 | (7) |
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5 Intelligent fault diagnosis for dynamic systems via extended state observer and soft computing |
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182 | (1) |
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5.2 Extended state observer |
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183 | (5) |
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5.3 Case study: three-tank dynamic system |
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188 | (4) |
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5.4 Fault detection by means of ESO |
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192 | (2) |
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5.5 Fault isolation and fault identification |
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194 | (3) |
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5.6 Simultaneous faults of different types |
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197 | (3) |
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5.7 Isolation of simultaneous process faults and actuator faults |
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200 | (3) |
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5.8 Conclusion and future work |
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203 | (1) |
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204 | (3) |
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6 Fault diagnosis and failure prognosis in hydraulic systems |
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6.1 Application status of sensor detection technology |
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207 | (10) |
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217 | (12) |
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6.3 Intelligent evaluation and diagnosis technology |
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229 | (15) |
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244 | (9) |
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253 | (11) |
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7 Fault detection and fault identification in marine current turbines |
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7.1 The HT-based detection method |
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264 | (5) |
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7.2 The wavelet threshold denoising-based dectection method |
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269 | (14) |
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7.3 The identification method of blade attachment based on the sparse autoencoder and softmax regression |
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283 | (7) |
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7.4 The identification method of blade attachment based on depthwise separable CNN |
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290 | (9) |
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7.5 Conclusion and future works |
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299 | (1) |
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300 | (5) |
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8 Quadrotor actuator fault diagnosis and accommodation based on nonlinear adaptive state observer |
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305 | (2) |
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8.2 Mathematical model of a quadrotor |
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307 | (2) |
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309 | (10) |
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319 | (4) |
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323 | (1) |
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323 | (4) |
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9 Defect detection and classification in welding using deep learning and digital radiography |
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327 | (6) |
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333 | (3) |
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336 | (1) |
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336 | (9) |
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9.5 Experimental implementation |
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345 | (1) |
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346 | (1) |
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347 | (6) |
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10 Real-time fault diagnosis using deep fusion of features extracted by PeLSTM and CNN |
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353 | (3) |
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356 | (1) |
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10.3 Deep fusion of feature extracted by PeLSTM and CNN |
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357 | (14) |
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10.4 Experimental testing |
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371 | (24) |
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10.5 Conclusion and future work |
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395 | (3) |
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398 | (1) |
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398 | (3) |
| Index |
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401 | |
Dr. Karimi received the B.Sc. (First Hons.) degree in power systems from the Sharif University of Technology, Tehran, Iran, in 1998, and the M.Sc. and Ph.D. (First Hons.) degrees in control systems engineering from the University of Tehran, Tehran, in 2001 and 2005, respectively. His research interests are in the areas of control systems/theory, mechatronics, networked control systems, intelligent control systems, signal processing, vibration control, ground vehicles, structural control, wind turbine control and cutting processes. He is an Editorial Board Member for some international journals and several Technical Committee. Prof. Karimi has been presented a number of national and international awards, including Alexander-von-Humboldt Research Fellowship Award (in Germany), JSPS Research Award (in Japan), DAAD Research Award (in Germany), August-Wilhelm-Scheer Award (in Germany) and been invited as visiting professor at a number of universities in Germany, France, Italy, Poland, Spain, China, Korea, Japan, India.
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