Bearing Semi-Supervised Anomaly Detection Using Only Normal Data

Bearings are ubiquitous machinery parts. Monitoring and diagnosing their state is essential for reliable functioning. Machine learning techniques are now established tools for anomaly detection. We focus on a less used setup, although a very natural one: the data available for training come only from normal behavior, as the faults are various and cannot be all simulated. This setup belongs to semi-supervised learning, and the purpose is to obtain a method that is able to distinguish between normal and faulty data. We focus on the Case Western Reserve University (CWRU) dataset, since it is relevant for bearing behavior. We investigate several methods, among which one based on Dictionary Learning (DL) and another using graph total variation stand out; the former was less used for anomaly detection, and the latter is a new algorithm. We find that, together with Local Factor Outlier (LOF), these algorithms are able to identify anomalies nearly perfectly, in two scenarios: on the raw time-domain data and also on features extracted from them.