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.

提出了一种基于自监督学习的神经网络模型来解决电动机故障诊断的问题，通过微调模型在不同故障情景、不同机器之间实现了超过 90％的分类准确度，具有广泛的潜力应用于现实世界中的跨机器故障诊断任务。