Metaheuristics and Support Vector Data Description for Fault Detection in Industrial Processes

Abstract

In this study, a system for faults detection using a combination of Support Vector Data Description (SVDD) with metaheuristic algorithms is presented. The presented approach is applied to a real industrial process where the set of measured faults is scarce. The original contribution in this work is the industrial context of application and the comparison of swarm intelligence algorithms to optimize the SVDD hyper-parameters. Four recent metaheuristics are compared hereby to solve the corresponding optimization problem in an efficient manner. These optimization techniques are then implemented for fault detection in a multivariate industrial process with non-balanced data. The obtained numerical results seem to be promising when the considered optimization techniques are combined with SVDD. In particular, the Spotted Hyena algorithm outperforms other metaheuristics reaching values of F1 score near 100% in fault detection.

Deep Multiscale Soft-Threshold Support Vector Data Description for Enhanced Heavy-Duty Gas Turbine Generator Sets’ Anomaly Detection

“…Te support vector data description model was used for feature extraction and hypersphere training, enabling the detection of abnormal points at a microgranularity level. Navarro-Acosta et al [18] applied a fault detection system that combines SVDD with metaheuristic algorithms to a real-world industrial process with a limited number of measured faults. Te primary contribution of this research is the comparison of various swarm intelligence algorithms for efectively optimizing the SVDD hyperparameters.…”