Active Foundational Models for Fault Diagnosis of Electrical Motors
Sriram Anbalagan Sai Shashank GP Deepesh Agarwal Balasubramaniam Natarajan Babji Srinivasan
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.