A new key performance indicator oriented industrial process monitoring and operating performance assessment method based on improved Hessian locally linear embedding

ABSTRACT

The industrial process monitoring and operating performance assessment techniques are of great significance to ensure the safety and efficiency of the production and to improve the comprehensive economic benefits for the modern enterprises. In this paper, a new key performance indicator (KPI) oriented nonlinear process monitoring and operating performance assessment method is proposed based on the improved Hessian locally linear embedding (HLLE), in view of the problems of strong nonlinearity, high dimension and information redundancy in actual industrial process data. Firstly, in order to characterise the similarities of samples in both temporal and spatial dimensions, a new measurement, based on Finite Markov theory, is defined to replace the Euclidean distance in traditional HLLE. Secondly, by mining the relationships between process variables and the key performance indicator, the KPI oriented feature extraction method is developed. On this basis, the monitoring statistics is constructed and the corresponding control limit is determined for the real-time fault detection. After that, a new operating performance assessment approach based on sliding window Kullback–Leibler divergence is put forward to facilitate maintenance or adjustments. Finally, the proposed method is applied to the hot strip mill process, and the results show the effectiveness.

KEYWORDS:

• Nonlinear process monitoring
• industrial operating performance assessment
• Hessian locally linear embedding
• Kullback–Leibler divergence
• hot strip mill process

Disclosure statement

No potential conflict of interest was reported by the authors.

Data availability statement

The data that support the findings of this study are available from the corresponding author, Kaixiang Peng, upon reasonable request.

Additional information

Funding

This work was supported by the National Key R&D Program of China (No. 2021YFB3301200) and Natural Science Foundation of China (NSFC) under Grants (U21A20483, 61873024). Also thanks for Fundamental Research Funds for the China Central Universities of USTB (FRF-TP-19-049A1Z), PR China for funding.

Notes on contributors

Hongjun Zhang

Hongjun Zhang received his B.E. degree in electrical automation from Zhejiang University, China. He received his M.E. degree in material engineering from Northeastern University, China. He is currently a Senior Engineer in Ansteel Group Corporation Limited, Anshan, Liaoning Province, and a Ph.D. candidate at the School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing, China. His research interests include industrial process monitoring and intelligent optimization.

Chi Zhang

Chi Zhang received his B.E. degree in measurement and control technology and instrumentation from Harbin University of Science and Technology, Harbin, China, in 2018. He received his M.E. degree in control science and engineering from University of Science and Technology Beijing, in 2021. He is now a Ph.D. candidate at the School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing, China. His research interests include industrial process monitoring, fault diagnosis, data analytics, and machine learning.

Jie Dong

Jie Dong received her B.E., M.E., and Ph.D. degrees from University of Science and Technology Beijing, in 1995, 1997, and 2007, respectively. She is currently a Professor of the School of Automation and Electrical Engineering, University of Science and Technology Beijing. From July to December in 2004, she visited University of Manchester as a visiting scholar. Her research interest covers intelligent control theory and application, process monitoring and fault diagnosis, complex system modeling and control.

Kaixiang Peng

Kaixiang Peng received his B.E. degree in automation and M.E. and Ph.D. degree from the Research Institute of Automatic Control, University of Science and Technology, Beijing, China, in 1995, 2002 and 2007, respectively. He is a Professor in the School of automation and electrical engineering, University of Science and Technology, Beijing, China. His research interests are fault diagnosis, prognosis, and maintenance of complex industrial processes, modeling and control for complex industrial processes, and control system design for the rolling process.