Fault diagnosis is essential in industrial processes for monitoring the conditions of important machines. With the ever-increasing complexity of working conditions and demand for safety during production and operation, different diagnosis methods are required, and more importantly, an integrated fault diagnosis system that can cope with multiple tasks is highly desired. However, the diagnosis subtasks are often studied separately, and the currently available 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, such as fault detection, fault classification, and novel fault diagnosis. Additionally, a unified fault diagnosis method based on internal contrastive learning is put forward to underpin the proposed generalized framework. The method extracts features utilizing the internal contrastive learning technique and then recognizes the outliers based on the Mahalanobis distance. Experiments are conducted on a simulated benchmark dataset as well as two practical process datasets to evaluate the proposed framework. As demonstrated in the experiments, the proposed method achieves better performance compared with several existing techniques and thus verifies the effectiveness of the proposed framework.

翻译：故障诊断对于工业过程中重要机器运行状态的监测至关重要。随着工作条件的日益复杂以及生产运行过程中对安全性的要求不断提高，需要采用不同的诊断方法。更重要的是，迫切需要一种能够处理多项任务的集成化故障诊断系统。然而，诊断子任务通常被分开研究，现有方法在构建这种通用系统方面仍有改进空间。为解决这一问题，我们提出了广义分布外故障诊断（GOOFD）框架，用于集成故障检测、故障分类和新异故障诊断等诊断子任务。此外，提出了一种基于内部对比学习的统一故障诊断方法，以支撑所提出的广义框架。该方法利用内部对比学习技术提取特征，然后基于马氏距离识别异常值。为评估所提框架，在模拟基准数据集以及两个实际过程数据集上进行了实验。实验结果表明，与现有多种技术相比，所提方法取得了更优性能，从而验证了所提框架的有效性。