Machine learning and deep learning modules have excelled in fault detection and diagnosis. Fault detection involves verifying the presence of faults through unsupervised methods utilizing anomaly detection algorithms. Fault diagnosis encompasses three fundamental tasks: determining equipment normalcy, identifying incipient faults and their causes, and predicting fault development trends. Once an anomaly is detected, a challenge arises in distinguishing it as a fault. That represents an additional challenge when doing fault detection since AI models might require a large volume of data labeled for both normal and fault conditions.
(Source: An Explainable Artificial Intelligence Approach for Unsupervised Fault Detection and Diagnosis in Rotating Machinery)