Fault detection and diagnosis of electrical motors are of utmost importance in ensuring the safe and reliable operation of several industrial systems. Detection and diagnosis of faults at the incipient stage allows corrective actions to be taken in order to reduce the severity of faults. The existing data-driven deep learning approaches for machine fault diagnosis rely extensively on huge amounts of labeled samples, where annotations are expensive and time-consuming. However, a major portion of unlabeled condition monitoring data is not exploited in the training process. To overcome this limitation, we propose a foundational model-based Active Learning framework that utilizes less amount of labeled samples, which are most informative and harnesses a large amount of available unlabeled data by effectively combining Active Learning and Contrastive Self-Supervised Learning techniques. It consists of a transformer network-based backbone model trained using an advanced nearest-neighbor contrastive self-supervised learning method. This approach empowers the backbone to learn improved representations of samples derived from raw, unlabeled vibration data. Subsequently, the backbone can undergo fine-tuning to address a range of downstream tasks, both within the same machines and across different machines. The effectiveness of the proposed methodology has been assessed through the fine-tuning of the backbone for multiple target tasks using three distinct machine-bearing fault datasets. The experimental evaluation demonstrates a superior performance as compared to existing state-of-the-art fault diagnosis methods with less amount of labeled data.

翻译：电机故障检测与诊断对于确保多个工业系统的安全可靠运行至关重要。在故障初期阶段进行检测与诊断，可以采取纠正措施以降低故障严重程度。现有的基于数据驱动的深度学习方法进行机器故障诊断时，严重依赖大量标注样本，而标注过程耗时且成本高昂。然而，大部分未标注的状态监测数据在训练过程中未被充分利用。为克服这一局限，我们提出了一种基于基础模型的主动学习框架，该框架通过有效结合主动学习与对比自监督学习技术，利用数量较少但信息量最大的标注样本，并充分利用大量可用的未标注数据。该框架包含一个基于Transformer网络的骨干模型，该模型采用先进的最近邻对比自监督学习方法进行训练。这种方法使骨干模型能够从原始未标注振动数据中学习到更优的样本表征。随后，该骨干模型可通过微调来应对同一机器及跨机器的各种下游任务。通过使用三个不同的机器轴承故障数据集对骨干模型进行多目标任务微调，评估了所提方法的有效性。实验评估表明，与现有的最优故障诊断方法相比，该方法在标注数据量更少的情况下展现出更优越的性能。