Data-driven Fault Diagnosis for Complex Industrial Processes: Towards Fault Prediction, Detection and Identification: 22 - Rilegato

Yin, Hongpeng; Zhou, Han; Chai, Yi; Tang, Qiu

Rilegato

ISBN 10: 9819631521 ISBN 13: 9789819631520

Casa editrice: Springer Nature, 2025

Riassunto

This book summarizes techniques of fault prediction, detection, and identification, all included specifically in the data-driven fault diagnosis requirements within industrial processes, drawing from the combination of data science, machine learning, and domain-specific expertise. In the modern industrial processes, where efficiency, productivity, and safety stand as paramount pillars, the pursuit of fault diagnosis has become more crucial than ever. The widespread use of computer systems, along with new sensor hardware, generates significant quantities of real-time process data. It has been frequently asked what could be done with both the real-time and archived historical data, to not only promising efficiency but providing prospect of a brighter, more resilient future. This book starts with the definition, related work, and open test-bed for industrial process fault diagnosis. Then, it presents several data-driven methods on fault prediction, fault detection, and fault diagnosis, with consideration of properties of industrial processes, such as varying operation modes, non-Gaussian, nonlinearity. It distills cutting-edge methodologies and insights which may inspire for industrial practitioners, researchers, and academicians alike.

Su questo libro

Informazioni sull?autore

Hongpeng Yin received his Ph.D. degree from Chongqing University in 2009. He is currently a professor at Chongqing University. His research interests include data processing and analysis and its application in space engineering, industrial manufacturing, public security, medical treatment, etc. He has published more than 50 SCI/ EI indexed papers, including TII, TASE, information fusion, etc. He was awarded as an outstanding reviewer of information fusion, information sciences, and other journals. He is authorized over 20 invention patents and 15 software copyrights. He is awarded 4 provincial awards and is selected as Chongqing elite young top talent and Chongqing university scientific research top talent.

 

Zhou Han received his doctoral degree (with honors) in 2024, from Chongqing University, China. He was also a special research student at the University of Tokyo from 2021 to 2023. He is currently a research fellow with Pengcheng Laboratory, Shenzhen, China. His research interests include machine learning, data mining and their applications on industrial health management. He has authorized more than 10 papers in top-tier journals/conferences, such as TII, RESS, IJCAI, and 4 patents. He received various awards/honors such as CAA Invention Award (2022), SMC and CSC Scholarship (2016, 2022–2023). He is the reviewer of IJIS, TII, PR, TAI, CCDC.

 

Yi Chai received the Ph.D. degree in Department of Automation in Chongqing University, Chongqing, China, in 2001. He is currently a professor at Chongqing University. His main research interests are the nonlinear dynamic systems, signal processing, information fusion, fault detection and diagnosis, intelligence systems. He has published over 200 papers and holds more than 20 authorized invention patents. Additionally, he has authored two monographs and has received six provincial awards. 

 

Qiu Tang received her Ph.D. degree in Automation College from Chongqing University, Chongqing, in 2020. She was a postdoctoral researcher at the School of Control Science and Engineering at Shandong University, from 2021 to 2023. She has been an editor in Shandong University Scientific Journals Press, Shandong University. Her research interests include process monitoring, manifold learning and fault diagnosis.

Dalla quarta di copertina

This book summarizes techniques of fault prediction, detection, and identification, all included specifically in the data-driven fault diagnosis requirements within industrial processes, drawing from the combination of data science, machine learning, and domain-specific expertise. In the modern industrial processes, where efficiency, productivity, and safety stand as paramount pillars, the pursuit of fault diagnosis has become more crucial than ever. The widespread use of computer systems, along with new sensor hardware, generates significant quantities of real-time process data. It has been frequently asked what could be done with both the real-time and archived historical data, to not only promising efficiency but providing prospect of a brighter, more resilient future. This book starts with the definition, related work, and open test-bed for industrial process fault diagnosis. Then, it presents several data-driven methods on fault prediction (Part I), fault detection (Part II), and fault diagnosis (Part III), with consideration of properties of industrial processes, such as varying operation modes, non-Gaussian, nonlinearity. It distills cutting-edge methodologies and insights which may inspire for industrial practitioners, researchers, and academicians alike.

Dettagli dell'edizione

• Editore: Springer Nature
• Data di pubblicazione: 2025
• Lingua: Inglese
• ISBN 10: 9819631521
• ISBN 13: 9789819631520
• Rilegatura: Copertina rigida
• Numero di pagine: 224
• Contatto del produttore: Elevation; https://www.also.com/ec/cms5/en_6000/6000/index.jsp; ; Friedrich-Karl-Str. 9; Hamburg; 22391; Germania