This book discusses condition based monitoring of rotating machines using intelligent adaptive systems. The book employs computational intelligence and fuzzy control principles to deliver a module that can adaptively monitor and optimize machine health and performance. This book covers design and performance of such systems and provides case studies and data models for fault detection and diagnosis. The contents cover everything from optimal sensor positioning to fault diagnosis. The principles laid out in this book can be applied across rotating machinery such as turbines, compressors, and aircraft engines. The adaptive fault diagnostics systems presented can be used in multiple time and safety critical applications in domains such as aerospace, automotive, deep earth and deep water exploration, and energy.

Takes an application oriented approach towards condition based monitoring Covers data collections and analyses based methodologies for condition based maintenance strategies and techniques Presents a detailed study from sensor positioning to detection of fault

Autorentext
Dr. Nishchal K. Verma (SM'13) is a Professor in Department of Electrical Engineering and Inter-disciplinary Program in Cognitive Science at Indian Institute of Technology Kanpur, India. He obtained PhD in Electrical Engineering from Indian Institute of Technology Delhi, India. He is an awardee of Devendra Shukla Young Faculty Research Fellowship by Indian Institute of Technology Kanpur, India for year 2013-16.

His research interests include intelligent fault diagnosis systems, prognosis and health management, big data analysis, deep learning of neural and fuzzy networks, machine learning algorithms, computational intelligence, computer vision, brain computer/machine interface, intelligent informatics, soft-computing in modelling and control, internet of things/ cyber physical systems, and cognitive science. He has authored more than 200 research papers.

Dr. Verma is an IETE Fellow. He is currently serving as a Guest Editor of the IEEE Access: special section on "Advance in Prognostics and System Health Management", an Editor of the IETE Technical Review Journal, an Associate Editor of the IEEE Transactions on Neural Networks and Learning Systems, an Associate Editor of the IEEE Computational Intelligence Magazine, an Associate Editor of the Transactions of the Institute of Measurement and Control, U.K. and editorial board member for several journals and conferences.

Dr. Al Salour is a Boeing Technical Fellow and the enterprise leader for the Network Enabled Manufacturing technologies. He is responsible for systems approach to develop, integrate, and implement affordable sensor based manufacturing strategies and plans to provide real time data for factory systems and supplier networks. He is building a model for the current and future Boeing factories by streamlining and automating data management to reduce factory direct labour and overhead support and promote manufacturing as a competitive advantage.

Dr. Salour's accomplishments include machine health monitoring integrations, asset tracking and RFID system installations; and safety systems for automated guided vehicles. Dr. Salour is the research investigator with national and international premiere universities and research labs. He serves as a committee vice chair for the ASME's prognostics and health manaement national society. He is also a member of Industrial wireless technical working group with the National Institute of Standards and Technology (NIST). 

Dr. Salour has 31 invention disclosures, 22 patents and 1 trade secret in manufacturing technologies.

Inhalt

Introduction.- Faults And Data Acquisition.- Preprocessing.- Feature Extraction.- Feature Selection.- Fault Recognisition.- Fault Diagnosis System For Air Compressor Using Palmtop.- Improved Fault Detection Model.- Fault Diagnosis System Using Smartphone.- References.