A successful example of machine learning-based anomaly detection for predictive maintenance comes from San Diego Gas & Electric. This public utility company faced a widespread energy leakage problem.

Unsupervised anomaly detection uses an unlabeled test set of data. It involves training a machine learning (ML) model to identify normal behavior using an unlabeled dataset.

The Move To Unsupervised Learning: Anomaly Detection As fraud became more complex, supervised learning alone wasn’t sufficient. This led to the adoption of unsupervised learning, particularly ...