Generalized Out-of-distribution Fault Diagnosis (GOOFD) via Internal Contrastive Learning
This addresses the need for a unified fault diagnosis system in industrial processes, though it appears incremental as it builds on existing methods for known bottlenecks.
The paper tackles the problem of integrating multiple fault diagnosis subtasks into a generalized system for industrial machines, proposing the GOOFD framework with internal contrastive learning and Mahalanobis distance, which achieves better performance than existing single-task methods on benchmark and practical datasets.
Fault diagnosis is crucial in monitoring machines within industrial processes. With the increasing complexity of working conditions and demand for safety during production, diverse diagnosis methods are required, and an integrated fault diagnosis system capable of handling multiple tasks is highly desired. However, the diagnosis subtasks are often studied separately, and the current methods still need improvement for such a generalized system. To address this issue, we propose the Generalized Out-of-distribution Fault Diagnosis (GOOFD) framework to integrate diagnosis subtasks. Additionally, a unified fault diagnosis method based on internal contrastive learning and Mahalanobis distance is put forward to underpin the proposed generalized framework. The method involves feature extraction through internal contrastive learning and outlier recognition based on the Mahalanobis distance. Our proposed method can be applied to multiple faults diagnosis tasks and achieve better performance than the existing single-task methods. Experiments are conducted on benchmark and practical process datasets, indicating the effectiveness of the proposed framework.