Minimalist module analysis for fault detection and localization

Abstract

Traditional multivariate statistical-based process monitoring (MSPM) methods are effective data-driven approaches for monitoring large-scale industrial processes, but have a shortcoming in handling the redundant correlations between process variables. To address this shortcoming, this study proposes a new MSPM method called minimalist module analysis (MMA). MMA divides process data into several different minimalist modules and one more independent module. All variables in the minimalist module are strongly correlated, and no redundant variables exist; therefore, the extracted feature components in one minimalist module will not be disturbed by noise from the other modules. This study also proposes new monitoring indices and a fault localization strategy for MMA, and simulation tests demonstrate that MMA achieves superior performance in fault detection and localization.

An Analysis Model of Performance Index Based on Weighted Fuzzy Mutual Information

“…In this method, expert scoring has strong subjective factors. Considering the redundancy between indicators, Lou Zhijiang et al [10] used the method of mutual information to discretize continuous data and combined with key performance indicators to discretize the data into a limited number, which has limitations. Liao Shujiao et al [11] considered the differences between different features, established a neighborhood rough set theoretical framework for feature granularity selection.…”