Intelligent Machinery Fault Diagnostics And Prognostics
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Intelligent Machinery Fault Diagnostics and Prognostics
The field of machinery maintenance is undergoing a remarkable transformation, driven by the convergence of intelligent technologies and data-driven approaches. This book delves into the fascinating world of intelligent machinery fault diagnostics and prognostics, exploring how these advancements are reshaping the way we monitor, diagnose, and predict faults in machinery. Intelligent Machinery Fault Diagnostics and Prognostics: The Future of Smart Manufacturing uses an interdisciplinary approach to provide a well-rounded understanding of smart manufacturing. It discusses cutting-edge smart manufacturing technologies and encompasses various aspects, from sensors and data analytics to predictive maintenance. The book offers real-world case studies illustrating how these innovations are successfully implemented in industrial settings and includes practical guidelines and methodologies that facilitate the implementation of solutions. The book also highlights the scalability and adaptability of this approach to different industries and manufacturing environments. Whether this book is for industry professionals, students, or researchers, readers can leverage the book’s insights to optimize machinery performance, minimize downtime, reduce costs, and improve safety in their respective industries.
Intelligent Fault Diagnosis and Remaining Useful Life Prediction of Rotating Machinery
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. - 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
Introduction of Intelligent Machine Fault Diagnosis and Prognosis
Condition monitoring, fault diagnosis and prognosis of machinery have received considerable attention in recent years and they are increasingly becoming important in industry because of the need to increase reliability and decrease possible loss of production due to the fault of equipments. Early fault detection, diagnosis and prognosis can increase equipment availability and performance, reduce consequential damage, prolong machine life and reduce spare parts inventories and break down maintenance. With the development of the artificial intelligence techniques, many intelligent systems have been employed to assist the maintenance management task to correctly interpret the fault data. The book is very easy to study; even if the reader is a beginner in the fault diagnosis area, they do not need special prerequisite knowledge to understand the contents of this book. The book is equipped with software under MATLAB and offers many examples which are related to fault diagnosis processes. It will be very useful to readers who want to study feature-based intelligent machine fault diagnosis and prognosis techniques. The book is dedicated to graduate students of mechanical and electrical engineering, computer science and for practising engineers.