The practitioners are concerned with strip-thickness relevant faults of steel-making cold-rolling continuous annealing process (CAP) which is a typical dynamic nonlinear process. In this paper, a novel data-driven dynamic concurrent kernel canonical correlation analysis (DCKCCA) approach is proposed for the diagnosis of the CAP strip thickness relevant faults. First, a DCKCCA algorithm is proposed to capture dynamic nonlinear correlations between strip thickness and process variables. Strip thickness specific variations, process-specific variations, and thickness-process covariations are monitored respectively. Secondly, a multi-block extension of DCKCCA is designed to compute the contributions according to block partition of lagged variables, in order to help localize faults relevant to abnormal strip thickness. Finally, the proposed methods are illustrated by the application to a real continuous annealing process.
Bibliographical noteSupport to this research was provided by the Natural Science Foundation of China (61673097, 61304107, 61490704, 61573022), the China Postdoctoral Science Foundation (2013M541242), the International Postdoctoral Exchange Fellowship Program (20130020) the Fundamental Disciplinary Research Program of the Shenzhen Committee on Science and Innovation (20160207), the Texas–Wisconsin–California Control Consortium (TWCCC), and the Fundamental Research Funds for the Central Universities (N160804002, N160801001), the National High-Tech. Research and Development Program of China (2015AA043802).
- Dynamic concurrent kernel canonical correlation analysis
- Dynamic nonlinear process
- Fault diagnosis
- Process modeling
- Process monitoring