Abstract
Due to the non-stationarity in a transition process, it is impossible to implement the monitoring using conventional statistical algorithms. In this paper, a novel identification and monitoring method for transitions using the mutual information similarity analysis (MISA) is proposed to cope with the problem. In the MISA method, the difference between different stable modes as well as transitions is identified. Therefore, the whole process can be divided into several small sub-segments. Considering the dynamic information contained in the process, the classical DPLS model is utilized for online identification and monitoring. Finally, the proposed algorithm is tested using the TE benchmark.
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