In modern industries, fault diagnosis has been widely applied with the goal of realizing predictive maintenance. The key issue for the fault diagnosis system is to extract representative characteristics of the fault signal and then accurately predict the fault type. In this paper, we propose a Causal Disentanglement Hidden Markov model (CDHM) to learn the causality in the bearing fault mechanism and thus, capture their characteristics to achieve a more robust representation. Specifically, we make full use of the time-series data and progressively disentangle the vibration signal into fault-relevant and fault-irrelevant factors. The ELBO is reformulated to optimize the learning of the causal disentanglement Markov model. Moreover, to expand the scope of the application, we adopt unsupervised domain adaptation to transfer the learned disentangled representations to other working environments. Experiments were conducted on the CWRU dataset and IMS dataset. Relevant results validate the superiority of the proposed method.

在现代工业中，故障诊断被广泛应用于实现预测性维护。本文提出了一种因果分离隐马尔可夫模型 (CDHM)，通过学习承载故障机制的因果关系，捕捉其特征以达到更强的表示能力。同时，我们利用时序数据逐步将振动信号分解为与故障相关和无关的因素，并通过重新优化 ELBO 来优化因果分离马尔可夫模型的学习。此外，为了扩大应用范围，我们采用无监督领域适应将学到的分离表示转移到其他工作环境。在 CWRU 数据集和 IMS 数据集上进行实验证实了该方法的优越性。

故障诊断的因果解缠隐藏马尔可夫模型