Adaptive unsupervised anomaly detection with dynamic recalibration for scalable industrial asset reliability monitoring
The findings show that a self-recalibrating unsupervised model can successfully detect faults in a scalable manner, with an interpretable model, low computational complexity and can adapt to ageing assets and varying operating baselines without labelled fault information.