A novel robust anomaly detection for enhancing reliability of industrial equipment under variable operating conditions via multi-sensor information fusion

Abstract Reliable anomaly detection is crucial for ensuring the operational safety and reducing maintenance costs of industrial equipment. However, existing anomaly detection methods for industrial equipment are limited in their ability to fuse multi-sensor data and adapt to varying operating conditions, leading to performance degradation from redundant information and poor cross-condition generalization. To address these challenges, this paper proposes a novel anomaly detection method based on information fusion of multiple operating conditions and multiple sensor sources (MOCSS), aiming to improve detection accuracy and robustness. Firstly, the proposed method employs a residual network to extract generalized features from multi-condition, multi-sensor data. Subsequently, a multi-head shrinkage graph attention network (MSGAT) is designed to adaptively learn the contribution of each sensor and suppress redundant information, achieving deep feature fusion. Furthermore, a multi-condition discriminator and a central moment discrepancy-based feature distribution constraint are introduced to align features across different operating conditions, enhancing cross-condition generalization. Finally, a one-class classifier is designed based on support vector data description to aggregate normal features into a high-dimensional hypersphere, increase the distance between abnormal features, and achieve efficient anomaly detection. Experiments on gear and 110 kV power transformer datasets validate the effectiveness of the proposed method. The results indicate that, compared with state-of-the-art methods, MOCSS demonstrates superior detection accuracy and noise robustness, achieving average accuracy improvements ranging from 11.67% to 29.64%, highlighting its potential for application in complex multi-condition and multi-sensor scenarios.