Quality-relevant fault detection and diagnosis for hot strip mill process with multi-specification and multi-batch measurements

Abstract

Modern hot strip mill process (HSMP) is increasingly characterized by producing small-batch, multi-specification and high-value-added products. Associated with such suffered complexities, performing valid process monitoring/fault diagnosis (PM-FD) is becoming a challenging task of ensuring process safety and product quality. In this paper, a new PM-FD approach based on principal component regression (PCR) is proposed for quality-relevant fault detection and diagnosis of HSMP. Firstly, the historical multi-batch process and quality variables data sets (three-dimensional) should be appropriately transferred into the availably applied two-dimensional data sets. Then, the presented approach could orthogonally project the process variable space into the quality-relevant part from the -irrelevant part. Next, when there comes a new measurement regarding a new batch from an unknown specification of the strip, it is automatically assigned into its preferential model by the prediction power of PCR integrated with Bayes inference. In depth, the detection and diagnosis are continued by the presented PCR based monitoring scheme. To the end, the new proposed scheme would be practiced with real HSMP data, where the individual steps as well as the complete framework were extensively tested.