Dissimilarity Analytics for Monitoring of Nonstationary Industrial Processes with Stationary Subspace Decomposition

The wide spread of dissimilarity analysis (DISSIM) gives rise to many useful process monitoring models. However, it is only limited to stationary processes. Reliable DISSIM based monitoring methods will encounter challenges of nonstationary behaviors including the time-varying mean or variance. In this work, a stationary subspace decomposition based DISSIM (SSD-DISSIM) model is developed to detect incipient faults sensitively for nonstationary industrial processes. As faults may disappear in time-varying process variations, the key is how to extract information with stationary characteristics from the complex process data. To eliminate the interference caused by the mixed nonstationary signals, the extraction of the stationary components is first conducted by projecting data into a low-dimensional subspace. As a type of distribution-based method, the DISSIM is combined to monitor the extracted stationary components in terms of not only the mean and variance, but also the correlations and distribution. Thus the proposed model can overcome the limitation of DISSIM that arises in nonstationary processes and enhance the sensitivity and reliability of the monitoring. The method effectiveness is demonstrated through the real thermal power plant example.