Variable Perturbation Based Fault Diagnosis for Industrial Process

Abstract

Real-time fault detection, classification and diagnosis in manufacturing and process industries is essential to prevent unplanned downtime and improve the reliability of industrial operations. While several well-accepted machine learning techniques exist for fault detection and classification, there is a need for a reliable and generalized fault diagnosis technique that identifies sensors responsible for industrial faults in real time. In this work, we propose a variable perturbation matrix-based method for fault diagnosis in industrial processes. We utilize the Long ShortTerm Memory for prediction due to its ability to memorize temporal information in time-series data. First, the fault is detected, then one or more independent variables are perturbed across the fault detection point to check the sensitivity of the diagnosis model for the corresponding variables. Thus, a perturbation matrix is calculated and variables with high sensitivity are selected as the variables responsible for fault. The proposed method is applied to an Industry 4.0 quality control test bed set up for electronic components, the dataset for which is provided in the Prognostics and Health Management Euorpe-21 (PHME-21) data challenge. The proposed method accurately detected and classified 6 faults in the test bed and correctly diagnosed the most significant variables. Due to high fault detection accuracy coupled with sensitivity-based fault diagnosis, the method is suitable for multivariate industrial systems.