Abstract:Industrial processes with high dimensional data are generally operated with mixed normal/faulty states in different modes which are difficult to be automatically and accurately identified. In this paper, a state identification framework is proposed for multimode processes. First, a key variable selection approach is presented based on sparse representation to eliminate redundant variables. Then, the modified density peak clustering (MDPC) is proposed to identify different states, in which a distance measuremen… Show more
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