Li Jie, Wang Yu, Zi Yanyang, Zhang Haijun, Wan Zhiguo
IEEE Trans Neural Netw Learn Syst. 2023 Sep;34(9):6250-6262. doi: 10.1109/TNNLS.2021.3135036. Epub 2023 Sep 1.
In recent years, the identification of out-of-distribution faults has become a hot topic in the field of intelligent diagnosis. Existing researches usually adopt domain adaptation methods to complete the generalization of diagnostic knowledge with the aid of target domain data, but the acquisition of fault samples in real industries is extremely time-consuming and costly. Moreover, most researches focus on samples with fixed fault levels, ignoring the fact that system degradation is a continuous process. In response to the above intractable problems, this article proposed a causal disentanglement network (CDN) to realize cross-machine knowledge generalization and continuous degradation mode diagnosis. In CDN, multitask instance normalization and batch normalization structure was proposed to learn task-specific knowledge and enhance the informativeness of the extracted features. On this basis, a causal disentanglement loss was proposed, which minimized the mutual information of features between subtask structures and captured the causal invariant fault information for better generalization. The experimental results proved the superiority and generalization ability of CDN, and the visualization results proved the performance of CDN in causality mining.
近年来，分布外故障识别已成为智能诊断领域的一个热门话题。现有研究通常采用域适应方法，借助目标域数据完成诊断知识的泛化，但在实际工业中获取故障样本极其耗时且成本高昂。此外，大多数研究集中在具有固定故障水平的样本上，忽略了系统退化是一个连续过程这一事实。针对上述棘手问题，本文提出了一种因果解缠网络（CDN），以实现跨机器知识泛化和连续退化模式诊断。在CDN中，提出了多任务实例归一化和批归一化结构来学习特定任务的知识，并增强提取特征的信息量。在此基础上，提出了一种因果解缠损失，它最小化了子任务结构之间特征的互信息，并捕获因果不变故障信息以实现更好的泛化。实验结果证明了CDN的优越性和泛化能力，可视化结果证明了CDN在因果关系挖掘方面的性能。
