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.

翻译：故障诊断对于监测工业过程中的设备至关重要。随着工况日益复杂和生产安全需求不断提升，需要多样化的诊断方法，且亟需能够处理多任务的集成式故障诊断系统。然而，现有诊断子任务通常被独立研究，当前方法对此类广义系统仍有待改进。为解决此问题，本文提出广义分布外故障诊断(GOOFD)框架以整合诊断子任务。同时，提出一种基于内部对比学习与马氏距离的统一故障诊断方法作为该广义框架的支撑。该方法通过内部对比学习进行特征提取，并基于马氏距离实现异常识别。所提方法可应用于多故障诊断任务，且性能优于现有单任务方法。在基准数据集和实际过程数据集上的实验验证了该框架的有效性。