Distributed Operating Performance Assessment for Hot Strip Mill Process Based on Probabilistic Support Tensor Data Description with Feature Tensor

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In industrial processes, operating performance assessment is of great practical significance for guiding the production adjustment for operators. From the perspective of classification, operating performance assessment is considered as a multi-class classification problem. As a well-known one-class classifier, support vector data description (SVDD) are oriented to vector data and cannot deal with tensor data directly. Moreover, SVDD gives the target data set a spherically shaped description, which is a binary output. However, practical industrial data of different operating performance grade may have overlapping region, which is a knotty problem for classification. To handle above issues, a distributed operating performance assessment method based on probabilistic support tensor data description (PSTDD) is proposed in this work. First, the plant-wide process variables are selected and divided into several blocks. Then, a PSTDD model is developed in each block. Based on the assessment results of different blocks, a global assessment index is designed. If the process is running at non-optimal condition, the root cause are traced by variable contributions. Experimental results on a real hot strip mill process (HSMP) illustrate the effectiveness of the proposed method comparing to the traditional distributed SVDD.