Anomaly detection methods for infrequent failures in resistive steel welding. (March 2022) - Meyer, Kevin; Mahalec, Vladimir
Abstract: Many industrial plants encounter infrequent faults and failures (anomalies) during operation which generate significant increases in costs. Modelling and detection of these anomalies is challenging due to unbalanced (few anomalous observations) and unlabeled (no class information) historical data. Previous works require large, labeled, and balanced training datasets which are typically not available in industry. This work relies solely on data taken from normal plant operation to construct anomaly detection models. Single-class neural network autoencoders and principal component analysis are developed as quality monitoring and anomaly detection solutions for resistive seam welding. Monitoring models of the welding process, which facilitate anomaly detection through interpretable metrics, are constructed from easily obtained normal welding process data. No examples of poor-quality welds, nor designed experiments, are required. The methods are applied to industrial data from a steel galvanizing line which encounters infrequent yet costly weld failures. It is shown that these two monitoring approaches are capable of robustly detecting infrequent weld-breaks, without requiring examples of weld failures or poor-quality welds, using only online industrial production data from normal welding operation. Additionally, the monitoring metrics, visualizations, and root-cause analysis functionality also provided by these models are demonstrated. Highlights: Infrequent bad resistive welds cause expensive disruption of galvanized steel production Scarce data on bad welds render supervised learning methods non-viable Single-class latent variable models only require normal process data for training Anomaly detection performance of these models is excellent on real industrial data Root cause diagnostic capabilities are enabled by latent variable models