دانلود کتاب تشخیص و پیش‌آگهی هوشمند خطا مبتنی بر داده‌های بزرگ برای سیستم‌های مکانیکی

With the increasing demands for high reliability, ensured operating safety and reduc-tion of maintenance costs, fault diagnosis and prognosis for mechanical systems, which are the key components in prognostics and health management (PHM), are becoming more and more important in the modern industries. In the past decades, theories and methodologies of diagnosis and prognosis have been successfully estab-lished for mechanical systems, which further promote the developments of different industries, including manufacturing, aviation and aerospace industries, transporta-tion, energy production, etc. Condition monitoring data are the foundation of diag-nosis and prognosis. As the sensing, data storage, and computing technologies advance, the big data of mechanical systems have been available with significantly increased data volume, data types, data intensity, etc., propelling diagnosis and prognosis into the Big Data Era.

The industrial big data pose great opportunities as well as big challenges for diag-nosis and prognosis of mechanical systems. It is promising to accurately identify and predict the machine health conditions through analyzing the big data. Mean-while, how to effectively process the real industrial massive and messy data still remains difficult. In the current literature, some existing books have contributed much effort in the area of diagnosis and prognosis. However, the Big Data-Driven Intelligent Fault Diagnosis and Prognosis for Mechanical Systems is still a nascent and open research issue. This book aims at bridging this gap and offering the first attempt in presenting the latest advancements in this area from both the academic and engineering perspectives.

This book contains the descriptions of the fundamental theories in mechanical fault diagnosis and prognosis, as well as the cutting-edge research in the recent years. The presented theories and methodologies are all demonstrated by experimental valida-tions or industrial applications in different fields. It is expected to offer a guideline for readers who are interested in the basic and advanced theories and methodologies of diagnosis and prognosis for mechanical systems. This book can be used as a textbook for graduate and senior undergraduate students in the related disciplines, and also an important reference for industrial engineers. Besides the mechanical engineering area, the presented works in this book can also benefit the other big data-related areas, such as computer science, electric engineering, automation science, power energy, etc.

The contents of this book were mostly developed at Xi’an Jiaotong University (XJTU), P. R. China. We would like to express sincere gratitude to our colleagues, Dr. Feng Jia, Dr. Liang Guo, Dr. Biao Wang, etc., who have provided invaluable contributions on the research works in this book. We also use this book to cherish the memory of Prof. Zhengjia He, who is the Ph.D. supervisor of the first author, Prof. Yaguo Lei. Even though he passed away in 2013 and has left us for 8 years, his insightful academic viewpoints on PHM are still the guidelines for our current and future research. Our memory of him will become more and more precious as time goes.