A Gaussian Feature Analytics-Based DISSIM Method for Fine-Grained Non-Gaussian Process Monitoring

Dissimilarity analysis (DISSIM) has been widely used to monitor the Gaussian processes. However, its further application is hindered due to its unavailability to non-Gaussian processes whose data do not satisfy the hypothesis of the Gaussian distributions. To sensitively detect faults and enhance understanding of the non-Gaussian processes, a Gaussian feature analytics-based DISSIM (GDISSIM) method is proposed to monitor both Gaussian information and non-Gaussian information concurrently. The key lies in the separation of information with different statistical properties mixed in the process data. Hence, Gaussian-feature-based analytics is lirst proposed to devise the extraction, representation, and analysis of the Gaussian information. Besides, multiple Gaussian clusters are estimated for the remained non-Gaussian information integrated with posterior probabilities, enabling both Gaussian information and non-Gaussian information to be readily monitored. Different from the methods based on specilic assumptions or approximations, the proposed GDISSIM scheme investigates both non-Gaussian information and Gaussian information and is, therefore, delined as a line-grained monitoring method. The practical utility and feasibility of the proposed method are verilied by a numerical case and a real thermal power plant process.