A majority of recent advancements related to the fault diagnosis of electrical motors are based on the assumption that training and testing data are drawn from the same distribution. However, the data distribution can vary across different operating conditions during real-world operating scenarios of electrical motors. Consequently, this assumption limits the practical implementation of existing studies for fault diagnosis, as they rely on fully labelled training data spanning all operating conditions and assume a consistent distribution. This is because obtaining a large number of labelled samples for several machines across different fault cases and operating scenarios may be unfeasible. In order to overcome the aforementioned limitations, this work proposes a framework to develop a foundational model for fault diagnosis of electrical motors. It involves building a neural network-based backbone to learn high-level features using self-supervised learning, and then fine-tuning the backbone to achieve specific objectives. The primary advantage of such an approach is that the backbone can be fine-tuned to achieve a wide variety of target tasks using very less amount of training data as compared to traditional supervised learning methodologies. The empirical evaluation demonstrates the effectiveness of the proposed approach by obtaining more than 90\% classification accuracy by fine-tuning the backbone not only across different types of fault scenarios or operating conditions, but also across different machines. This illustrates the promising potential of the proposed approach for cross-machine fault diagnosis tasks in real-world applications.

翻译：近期电机故障诊断领域的多数进展均基于训练数据与测试数据服从相同分布的假设。然而，在实际电机运行场景中，不同工况下的数据分布可能产生显著差异。这一假设导致现有故障诊断研究的实际应用受到制约——这些方法不仅需要涵盖所有工况的完备标注训练数据，还要求数据保持同分布特性。由于在跨故障类型和运行场景的多台机器上获取大量标注样本往往不可行，为突破上述局限，本文提出构建电机故障诊断基础模型的框架。该框架首先基于自监督学习训练神经网络骨干网络以提取高级特征，随后通过微调实现特定目标任务。该方法的显著优势在于：相比传统监督学习方法，骨干网络仅需极少量的训练数据即可通过微调适应多种目标任务。实验评估表明，所提方法不仅能在不同故障类型或运行工况下，更能跨越不同机器类型，通过微调骨干网络实现90%以上的分类准确率，充分验证了该方法在实际应用中实现跨机器故障诊断任务的巨大潜力。