Chang Rihao, Ma Yongtao, Nie Weizhi, Nie Jie, Zhu Yiqun, Liu An-An
IEEE Trans Neural Netw Learn Syst. 2024 Dec 16;PP. doi: 10.1109/TNNLS.2024.3513329.
In the predictive maintenance of modern industries, accurate fault diagnosis under complex conditions is now a major research focus. Recent research has demonstrated the effectiveness of deep learning in advancing bearing fault diagnosis. However, due to the scarcity of industrial failure data, achieving robust generalization in complex working conditions remains a challenge. To address this, we propose the causal disentanglement-based hidden Markov model (CDHM), which is designed to recognize the underlying causality in bearing vibration signals, capturing essential fault patterns for a more accurate and generalizable fault representation. Compared to signal-processing methods, deep learning approaches bypass the complex signal analysis, yet overlook the significance of signal theories in precise fault diagnosis. Nevertheless, the bearing vibration mechanism sheds light on the fact that the vibration induced by a certain type of fault has a consistent pattern across different system conditions, while the fault-irrelevant vibration such as noise and interference varies. Therefore, the CDHM constructs a time-series structural causal model (SCM), offering a new perspective on the interconnections of bearing vibration signals. Based on the SCM, a hidden Markovian variational autoencoder (VAE) is designed to progressively disentangle the vibration signal into two parts: a fault-relevant representation capturing essential bearing fault characteristics, and a fault-irrelevant representation capturing system and environmental interference. While unsupervised causal disentanglement typically presents optimization challenges, the CDHM benefits from cross-domain fault diagnosis tasks by leveraging the cross-domain consistency of the fault-relevant representation and the domain sensitivity of the fault-irrelevant representation. This design aligns the optimization objectives of causal disentanglement learning and cross-domain transfer learning, enabling mutually reinforcing optimization and ensuring robust generalization across diverse operating conditions. We validate the CDHM through experiments on the Case Western Reserve University (CWRU), Intelligent Maintenance System (IMS), and Paderborn University (PU) datasets, demonstrating its strong potential for industrial applications.
在现代工业的预测性维护中，复杂条件下的精确故障诊断是当前主要的研究重点。近期研究已证明深度学习在推进轴承故障诊断方面的有效性。然而，由于工业故障数据稀缺，在复杂工作条件下实现强大的泛化能力仍是一项挑战。为解决这一问题，我们提出了基于因果解缠的隐马尔可夫模型（CDHM），该模型旨在识别轴承振动信号中的潜在因果关系，捕捉基本故障模式以实现更准确、可泛化的故障表征。与信号处理方法相比，深度学习方法绕过了复杂的信号分析，但忽略了信号理论在精确故障诊断中的重要性。尽管如此，轴承振动机制表明，特定类型故障引起的振动在不同系统条件下具有一致的模式，而与故障无关的振动（如噪声和干扰）则有所不同。因此，CDHM构建了一个时间序列结构因果模型（SCM），为轴承振动信号的相互关系提供了新视角。基于SCM，设计了一个隐马尔可夫变分自编码器（VAE），将振动信号逐步解缠为两部分：捕捉轴承基本故障特征的与故障相关的表征，以及捕捉系统和环境干扰的与故障无关的表征。虽然无监督因果解缠通常存在优化挑战，但CDHM通过利用与故障相关表征的跨域一致性和与故障无关表征的域敏感性，从跨域故障诊断任务中受益。这种设计使因果解缠学习和跨域迁移学习的优化目标保持一致，实现相互强化的优化，并确保在各种运行条件下的强大泛化能力。我们通过在凯斯西储大学（CWRU）、智能维护系统（IMS）和帕德博恩大学（PU）数据集上的实验验证了CDHM，证明了其在工业应用中的强大潜力。
