Machinery prognostics and prognosis oriented maintenance management / / Jihong Yan.

• VerfasserIn: Yan, Jihong,
• Place / Publishing House: Singapore : : Wiley,, 2015.
• Year of Publication: 2015
• Language: English
• Physical Description: 1 online resource (356 pages) :; illustrations

"This book gives a complete presentation of the basic essentials of machinery prognostics and prognosis oriented maintenance management, and takes a look at the cutting-edge discipline of intelligent failure prognosis technologies for condition-based maintenance. Latest research results and application methods are introduced for signal processing, reliability moelling, deterioration evaluation, residual life prediction and maintenance-optimization as well as applications of these methods"-- Provided by publisher.

Table of Contents

Machine generated contents note: Preface i Acknowledgements i Chapter 1 Introduction 7 1.1 Historical perspective 7 1.2 Diagnostic and prognostic system requirements 8 1.3 Need for prognostics and sustainability based maintenance management 9 1.4 Technical challenges in prognosis and sustainability based maintenance decision making 11 1.5 Data processing, prognostics and decision making 13 1.6 Sustainability based maintenance management 16 1.7 Future of prognostics based maintenance 19 References 20 Chapter 2 Data processing 21 2.1 Probability Distributions 21 2.2 Statistics on Unordered data 32 2.3 Statistics on Ordered Data 38 2.4 Technologies for incomplete data 39 References 428 Chapter 3 Signal processing 45 3.1 Introduction 45 3.2 Signal pre-processing 47 3.3 Techniques for signal processing 50 3.4 Real-time image feature extraction 72 3.5 Fusion or integration technologies 77 3.6 Statistical pattern recognition and data mining 80 3.7 Advanced technology for feature extraction 92 References 102 Chapter 4 Health monitoring and prognosis 110 4.1 Health monitoring as a concept 110 4.2 Degradation indices 111 4.3 Real-time monitoring 116 4.4 Failure prognosis 142 4.5 Physics-based prognosis models 155 4.6 Data-driven prognosis models 158 4.7 Hybrid prognosis models 162 Reference 165 Chapter 5 Prediction of residual service life 172 5.1 Formulation of problem 172 5.2 Methodology of probabilistic prediction 173 5.3 Dynamic life prediction using time series 180 5.4 Residual life prediction by crack-growth criterion 197 References 202 Chapter 6 Maintenance planning and scheduling 205 6.1 Strategic planning in maintenance 205 6.2 Maintenance scheduling 219 6.3 Scheduling techniques 232 6.4 Heuristic methodology for multi-unit system maintenance scheduling 261 References 266 Chapter 7 Prognosis incorporating maintenance decision making 270 7.1 The changing role of maintenance 270 7.2 Development of maintenance 272 7.3 Maintenance effects modeling 274 7.4 Modeling of optimization objective - maintenance cost 282 7.5 Prognosis oriented maintenance decision making 284 7.6 Maintenance decision making considering energy consumption 301 References 317 Chapter 8 Case studies 321 8.1 Improved Hilbert-Huang transform based weak signal detection methodology and its application on incipient fault diagnosis and ECG signal analysis 322 8.2 Ant colony clustering analysis based intelligent fault diagnosis method and its application to rotating machinery 329 8.3 BP Neural Networks Based Prognostic Methodology and Its Application 336 8.4 A Dynamic Multi-scale Markov Model Based Methodology for Remaining Life Prediction 343 8.5 A group technology based methodology for maintenance scheduling for hybrid shop 358 References 365 Index 369.