Fault Detection: Classification, Techniques and Role in Industrial Systems


Fausto Pedro García Márquez, PhD and Mayorkinos Papaelias, PhD (Editors)
Birmingham University, UK

Series: Mechanical Engineering Theory and Applications
BISAC: TEC016000

This book synthesizes the principles of fault detection with a focus on interfaces between the main disciplines of methods/technologies and industrial systems. It is complementary to other sub-disciplines such as maintenance, engineering, safety, risk analysis, etc.
This book is intended for engineers, economists, technical consultants, researchers, etc. who are involved in the advancement of fault detection, and for those who incorporate fault detection in their work.

The authors of this volume describe their pioneering work in the area and provide material from case studies that successfully applied the fault detection discipline in real life cases. Some topics discussed include condition monitoring of wind turbines; induction motor fault detection based on vibration analysis and support vector machines; failure detection and accommodation approaches for the airspeed sensor on a small UAV; and model-based fault diagnosis for industrial mobile robots. (Imprint: Nova)



Table of Contents


Chapter 1. Condition Monitoring of Wind Turbines: Sensory Signals and Signal Processing Methods
(Fausto Pedro García Márquez, Jesús María Pinar Pérez and Raúl Ruiz de la Hermosa González-Carrato, Ingenium Research Group, Universidad Castilla-La Mancha, Ciudad Real, Spain, and others)

Chapter 2. An Integrated Strategy for Efficient Non-Destructive Evaluation of Rails
(M. Ph. Papaelias, Centre of Rail Research and Education, The University of Birmingham, UK)

Chapter 3. Data-Enabled Health Management of Complex Industrial Systems
(Soumik Sarkar, Soumalya Sarkar and Asok Ray, Mechanical Engineering Department, The Pennsylvania State University, PA, USA)

Chapter 4. Expert System for Induction Motor Fault Detection Based on Vibration Analysis and Support Vector Machines
(Željko S. Kanović, Faculty of Technical Sciences, Novi Sad, Serbia)

Chapter 5. Failure Detection and Accommodation Approaches for the Airspeed Sensor on a Small UAV
(Srikanth Gururajan, Matthew Rhudy, Mario L. Fravolini, Haiyang Chao and Marcello R. Napolitano, West Virginia University, Morgantown, WV, USA, and others)

Chapter 6. H∞ Fault Detection and Diagnosis of a Class of Descriptor Systems under Feedback Control
(Jun Xu, Advanced Servo Technology, Western Digital, Singapore)

Chapter 7. Intelligent Data Compression, Diagnostics and Prognostics Using An Evolutionary Based Clustering Algorithm for Industrial Machines
(Jun Chen, Michael Gallimore, Chris Bingham, Mahdi Mahfouf and Yu Zhang, School of Engineering, University of Lincoln, Lincoln, UK, and others)

Chapter 8. Model-Based Fault Diagnosis for Industrial Mobile Robots
(Andrea Monteriù and Sauro Longhi, Dipartimento di Ingegneria dell’Informazione, Università Politecnica delle Marche, Ancona, Italy)



“It is well known that the current scenarios differ from that ones we met decades ago. This is the result of prominent changes from a technological point of view that affects the maintenance field among others. One of the key factors in the Fault Detection and Diagnosis is the complexity to evaluate the type of failure and to implement suitable techniques.” READ MORE…Raúl Ruiz de la Hermosa González-Carrato, Escuela Técnica Superior de Ingenieros Industriales, Ciudad Real

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