LAUSR

Abstract In the modern hot strip mill process (HSMP), the operating performance may deteriorate because of wear of equipment, mode transitions, and random disturbances. If the process is not adjusted and maintained, faults may occur, resulting in greater economic losses and potential safety hazards. Therefore, it is of great practical significance to carry out comprehensive operating performance assessment. In this paper, a lifecycle operating performance assessment framework based on robust kernel canonical variable analysis (RKCVA) is proposed to deal with automation hierarchy, nonlinearity, and outliers in the plant-wide HSMP. First, the HSMP is divided into upstream, midstream, and downstream in real-time control level (L1). Then, based on kernel canonical variable analysis (KCVA) and partial robust M-regression (PRM), the RKCVA models are developed for each stream and process control level (L2). Based on the Bayesian inference, statistical fusion is implemented to judge whether the process is in normal or faulty operating condition. After that, according to different evaluation rules of different operating conditions, the lifecycle operating performance assessment is realized. Finally, the framework is illustrated with a case study on a real HSMP. The assessment results show that the accuracy of the RKCVA is more than 10% higher than that of the KCVA.