Based on AI and machine learning, this book systematically presents the theories and methods for complex electro-mechanical system fault prognosis, intelligent diagnosis, and health state assessment in modern industry. The book emphasizes feature extraction, incipient fault prediction, fault classification, and degradation assessment, which are based on supervised-, semi-supervised-, manifold-, and deep learning; machinery degradation state tracking and prognosis by phase space reconstruction; and complex electro-mechanical system reliability assessment and health maintenance based on running state info. These theories and methods are integrated with practical industrial applications, which can help the readers get into the field more smoothly and provide an important reference for their study, research, and engineering practice.

Expert guidance on theory and practice in condition-based intelligent machine fault diagnosis and failure prognosis Intelligent Fault Diagnosis and Prognosis for Engineering Systems gives a complete presentation of basic essentials of fault diagnosis and failure prognosis, and takes a look at the cutting-edge discipline of intelligent fault diagnosis and failure prognosis technologies for condition-based maintenance. It thoroughly details the interdisciplinary methods required to understand the physics of failure mechanisms in materials, structures, and rotating equipment, and also presents strategies to detect faults or incipient failures and predict the remaining useful life of failing components. Case studies are used throughout the book to illustrate enabling technologies. Intelligent Fault Diagnosis and Prognosis for Engineering Systems offers material in a holistic and integrated approach that addresses the various interdisciplinary components of the field--from electrical, mechanical, industrial, and computer engineering to business management. This invaluably helpful book: * Includes state-of-the-art algorithms, methodologies, and contributions from leading experts, including cost-benefit analysis tools and performance assessment techniques * Covers theory and practice in a way that is rooted in industry research and experience * Presents the only systematic, holistic approach to a strongly interdisciplinary topic

This book proposes a new approach to dynamic and online risk assessment of automated and autonomous marine systems, taking into account different environmental and operational conditions. The book presents lessons learnt from dynamic positioning incidents and accidents, and discusses the challenges of risk assessment of complex systems. The book begins by introducing dynamic and online risk assessment, before presenting automated and autonomous marine systems, as well as numerous dynamic positioning incidents. It then discusses human interactions with technology and explores how to quantify human error. Dynamic probabilistic risk assessment and online risk assessment are both considered fully, including case studies with the application of assisting operators in decision making in emergency situations. Finally, areas for future research are suggested. This practical volume offers tools and methodologies to help operators make better decisions and improve the safety of automated and autonomous marine systems. It provides a guideline for researchers and practitioners to perform dynamic probabilistic and online risk assessment, which also should be applicable to other complex systems outside the marine and maritime domain, such as nuclear power plants, chemical processes, autonomous transport systems, and space shuttles.

This book includes the original, peer-reviewed research papers from the 9th Frontier Academic Forum of Electrical Engineering (FAFEE 2020), held in Xi’an, China, in August 2020. It gathers the latest research, innovations, and applications in the fields of Electrical Engineering. The topics it covers including electrical materials and equipment, electrical energy storage and device, power electronics and drives, new energy electric power system equipment, IntelliSense and intelligent equipment, biological electromagnetism and its applications, and insulation and discharge computation for power equipment. Given its scope, the book benefits all researchers, engineers, and graduate students who want to learn about cutting-edge advances in Electrical Engineering.

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.

This book constitutes revised papers from the Second International Conference on Urban Intelligence and Applications, ICUIA 2020, held in August 2020. Due to the COVID-19 pandemic the conference was held online. The 26 papers were thoroughly reviewed and selected from 122 submissions. They are organised in the topical sections on technology and infrastructure; community and wellbeing; mobility and transportation; security, safety, and emergency management.

New engineering materials, techniques and applications are constantly being researched and developed, and keeping up to speed with the latest advances is crucial for engineers if they are to successfully address the challenges they face in their work. This book presents the selected proceedings of MMSE2023, the 9th International Conference on Advances in Machinery, Materials Science and Engineering Applications, jointly organized by the SAE-Supmeca, France and China University of Geosciences (Wuhan) and held on 22 and 23 July 2023 in Wuhan, China. For the past 12 years, this annual conference has collated recent advances and experiences, identified emerging trends and provided a platform for participants from academia and industry to exchange information and views, helping to address the world’s machinery and engineering challenges. The book contains 4 sections: mechanical engineering, material science and manufacturing technology; electrical engineering, automation and control; modeling, simulation and optimization techniques in engineering; and advanced engineering technologies and applications. A total of 241 submissions were received for MMSE2023, of which 151 papers were selected for the conference and for publication by means of a rigorous international peer-review process. These papers present exciting ideas and methods that will open novel research directions for different communities. Offering a current overview of the latest research and applications in machinery and materials-science engineering, the book will be of interest to all those working in the field.

Condition Monitoring Using Computational Intelligence Methods promotes the various approaches gathered under the umbrella of computational intelligence to show how condition monitoring can be used to avoid equipment failures and lengthen its useful life, minimize downtime and reduce maintenance costs. The text introduces various signal-processing and pre-processing techniques, wavelets and principal component analysis, for example, together with their uses in condition monitoring and details the development of effective feature extraction techniques classified into frequency-, time-frequency- and time-domain analysis. Data generated by these techniques can then be used for condition classification employing tools such as: • fuzzy systems; rough and neuro-rough sets; neural and Bayesian networks;hidden Markov and Gaussian mixture models; and support vector machines.