Adaptive unsupervised anomaly detection with dynamic recalibration for scalable industrial asset reliability monitoring
TL;DR
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.
Abstract
Anomaly-detection methods typically fail to detect changes in operating conditions or are unresponsive in detecting changes in the aging of industrial assets. This paper outlines an unsupervised lightweight machine learning-based approach to adaptively detect anomalies in the reliability of industrial assets. It incorporates in its framework operating-range normalisation, principal component analysis, Euclidean-distance anomaly scoring, Gaussian-based dynamic thresholds, sensor-contribution ranking and autonomous baseline recalibration. The validation was performed by taking 10 sensor variables initially screened in a critical P-101 A/B pump in an HDPE slurry polymerisation process, sampled approximately every 15 min and nine variables were selected for sensor-quality screening and then used for the validation. The framework identified all 44 reported functional failures resulting in a 100% recall, 84.62% precision, an average warning lead time of 72 h and an F1-score of 91.67%. 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.