Novel Pattern-Matching Integrated KCVA with Adaptive Rank-Order Morphological Filter and Its Application to Fault Diagnosis

With the scale expansion of industrial processes, the relationship between process variables has become complex and highly nonlinear. As a result, the requirements for fault diagnosis and safety monitoring has become demanding. To address this problem, a novel and effective pattern-matching method using kernel canonical variate analysis (KCVA) integrated with an adaptive rank-order morphological filter (ARMF) is proposed for fault diagnosis. In the proposed method, KCVA is first used to extract the nonlinear correlation information with dynamic characteristics from the original process data and achieve feature dimension reduction; the features extracted by KCVA are then subjected to ARMF transformation for output trend features and pattern matching. To accurately evaluate the morphological similarity between the test trends and template trends of ARMF, the dynamic time warping distance is adopted for pattern classification. Finally, the proposed KCVA-ARMF pattern-matching method is developed as an effective fault diagnosis model for complex industrial processes. To validate the performance of the proposed method, case studies using the Tennessee Eastman process are performed. Compared with some other multivariate statistical process monitoring methods, the simulation results indicate that the proposed KCVA-ARMF method can obtain higher accuracy in fault diagnosis, especially for difficult-to-diagnose faults.