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

Abstract

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 tra..