Intelligent Fault Diagnosis and Remaining Useful Life Prediction of Rotating Machinery
Auteur : Lei Yaguo
Intelligent Fault Diagnosis and Remaining Useful Life Prediction of Rotating Machinery provides a comprehensive introduction of intelligent fault diagnosis and RUL prediction based on the current achievements of the author's research group. The main contents include multi-domain signal processing and feature extraction, intelligent diagnosis models, clustering algorithms, hybrid intelligent diagnosis strategies, and RUL prediction approaches, etc.
This book presents fundamental theories and advanced methods of identifying the occurrence, locations, and degrees of faults, and also includes information on how to predict the RUL of rotating machinery. Besides experimental demonstrations, many application cases are presented and illustrated to test the methods mentioned in the book.
This valuable reference provides an essential guide on machinery fault diagnosis that helps readers understand basic concepts and fundamental theories. Academic researchers with mechanical engineering or computer science backgrounds, and engineers or practitioners who are in charge of machine safety, operation, and maintenance will find this book very useful.
1. Introduction and background2. Signal Processing and feature extraction3. Individual intelligent techniques based fault diagnosis4. Clustering algorithms based fault diagnosis5. Multidimensional hybrid intelligent diagnosis6. RUL prediction
Academic researchers at universities and other institutions, with mechanical engineering or computer science background, work toward the field of intelligent fault diagnosis and RUL prediction. Engineers or practitioners at companies take charge of machinery safe operation and maintenance.
- Provides a detailed background and roadmap of intelligent diagnosis and RUL prediction of rotating machinery, involving fault mechanisms, vibration characteristics, health indicators, and diagnosis and prognostics
- Presents basic theories, advanced methods, and the latest contributions in the field of intelligent fault diagnosis and RUL prediction
- Includes numerous application cases, and the methods, algorithms, and models introduced in the book are demonstrated by industrial experiences
Date de parution : 10-2016
Ouvrage de 376 p.
19x23.4 cm
Thèmes d’Intelligent Fault Diagnosis and Remaining Useful Life... :
Mots-clés :
adaptive neuro-fuzzy inference systems; artificial neural networks; clustering algorithm; data-driven methods; deep learning; diagnostics; empirical mode decomposition; exponential model; fault diagnosis; feature extraction; health monitoring; Hilbert-Huang transform; hybrid clustering; hybrid intelligent diagnosis methods; intelligent fault diagnosis; maintenance strategy; model-based methods; multilayer perception neural networks; multiple classifier combination; Paris-Erdogan model; particle filtering; polynomial model; predictive maintenance; prognostics; prognostics and health management; radial basis function neural networks; relevance vector machine; remaining useful life prediction; rotating machinery; signal processing; statistical learning theory; wavelet transform; weight fuzzy c-means; weight K nearest neighbor; weighted K nearest neighbor; Wigner-Ville distribution