Comprehensive Fault Diagnosis of Shaft Furnace Roasting Processes Using Simplified Concurrent Projection to Latent Structures

• LIU Qiang^{1,2 ,}  , 
• QIN S. Joe^{2,3 , ,} 

• 1.

  State Key Laboratory of Synthetical Automation for Process Industries, Northeastern University, Shenyang 110819, China

• 2.

  Department of Chemical Engineering and Materials Science, University of Southern California, Los Angeles, CA 90089, USA

• 3.

  School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen, Shenzhen 518172, China

Author Bio:

Associate professor at the State Key Laboratory of Synthetical Automation for Process Industries (Northeastern University), China, and postdoctor in the Department of Chemical Engineering, University of Southern California, USA. His research interest covers statistical process monitoring and fault diagnosis of complex industrial processes. Dr. Liu was the recipient of the Excellent Doctoral Dissertation of the Liaoning Province of China. He was also the recipient of the Excellent Doctoral Dissertation Nomination Award of Automation Society by the Automation Society of China

Corresponding author: QIN S. Joe  Professor at the University of Southern California, Los Angeles, USA. He is a Fellow of the International Federation of Automatic Control and a Fellow of IEEE. His research interest covers statistical process monitoring, fault diagnosis, model predictive control, system identification, building energy optimization, and control performance monitoring. Professor Qin was a recipient of the NSF CAREER Award, the NSF-China Outstanding Young Investigator Award, the Chang Jiang Professor of Tsinghua University, the Halliburton/Brown and Root Young Faculty Excellence Award, and the DuPont Young Professor Award. He is currently an associate editor of the Journal of Process Control and the IEEE Control Systems Magazine and a member of the editorial board of the Journal of Chemometrics. Corresponding author of this paper



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