A Dual-Task Learning Approach for Bearing Anomaly Detection and State Evaluation of Safe Region

Abstract

Predictive maintenance has emerged as an effective tool for curbing maintenance costs, yet prevailing research predominantly concentrates on the abnormal phases. Within the ostensibly stable healthy phase, the reliance on anomaly detection to preempt equipment malfunctions faces the challenge of sudden anomaly discernment. To address this challenge, this paper proposes a dual-task learning approach for bearing anomaly detection and state evaluation of safe regions. The proposed method transforms the execution of the two tasks into an optimization issue of the hypersphere center. By leveraging the monotonicity and distinguishability pertinent to the tasks as the foundation for optimization, it reconstructs the SVDD model to ensure equilibrium in the model's performance across the two tasks. Subsequent experiments verify the proposed method's effectiveness, which is interpreted from the perspectives of parameter adjustment and enveloping trade-offs. In the meantime, experimental results also show two deficiencies in anomaly detection accuracy and state evaluation metrics. Their theoretical analysis inspires us to focus on feature extraction and data collection to achieve improvements. The proposed method lays the foundation for realizing predictive maintenance in a healthy stage by improving condition awareness in safe regions.

AI-enabled industrial equipment monitoring, diagnosis and health management

“…In conclusion, the integration of AI-enabled industrial equipment monitoring, diagnosis, and health management is poised to significantly impact the future of predictive maintenance and operational efficiency in various industries. With the continued advancement of technologies such as transfer learning, explainable AI, digital twin, and reinforcement learning [43][44][45][46][47][48][49][50][51][52], we foresee a transformation in fault diagnosis, real-time monitoring, and proactive health management of industrial equipment. The application of these cutting-edge technologies in prognostics and health management is expected to revolutionize the industry, offering more precise predictions, minimized downtime, and enhanced equipment performance, ultimately leading to heightened productivity and cost savings.…”