Intelligent bearing diagnostic methods are developing rapidly, but they are difficult to implement due to the lack of real industrial data. A feasible way to deal with this problem is to train a network through laboratory data to mine the causality of bearing faults. This means that the constructed network can handle domain deviations caused by the change of machines, working conditions, noise, and so on which is, however, not a simple task. In response to this problem, a new domain generalization framework—Whitening-Net—was proposed in this article. This framework first defined the homologous compound domain signal as the data basis. Subsequently, the causal loss was proposed to impose regularization constraints on the network, which enhances the network’s ability to mine causality. To avoid domain-specific information from interfering with causal mining, a whitening structure was proposed to whiten the domain, prompting the network to pay more attention to the causality of the signal rather than the domain noise. The results of diagnosis and interpretation proved the ability of Whitening-Net in mining causal mechanisms, which shows that the proposed network can generalize to different machines, even if the tested working conditions and bearing types are completely different from the training domains.

中文翻译：

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Whitening-Net：用于诊断不同机器和条件之间故障的通用网络

智能轴承诊断方法发展迅速，但由于缺乏真实的工业数据，难以实施。解决这个问题的一个可行方法是通过实验室数据训练一个网络来挖掘轴承故障的因果关系。这意味着构建的网络可以处理由机器、工作条件、噪声等变化引起的域偏差，但这并不是一项简单的任务。针对这一问题，本文提出了一种新的域泛化框架——Whitening-Net。该框架首先将同源复合域信号定义为数据基础。随后，又提出了因果损失对网络施加正则化约束，增强了网络挖掘因果关系的能力。为了避免域特定信息干扰因果挖掘，提出了一种白化结构来对域进行白化，促使网络更加关注信号的因果关系而不是域噪声。诊断和解释的结果证明了 Whitening-Net 在挖掘因果机制方面的能力，这表明所提出的网络可以推广到不同的机器，即使测试的工作条件和轴承类型与训练域完全不同。