A Comparative Study and a New Industrial Platform for Decentralized Anomaly Detection Using Machine Learning Algorithms

Abstract

The occurrence of anomalies and unexpected, process-related faults is a major problem for manufacturing systems, which has a significant impact on product quality. Early detection of anomalies is therefore of central importance in order to create sufficient room for maneuver to take countermeasures and ensure product quality. This paper investigates the performance of machine learning (ML) algorithms for anomaly detection in sensor data streams. For this purpose, the performance of six ML algorithms (K-means, DBSCAN, Isolation Forest, OCSVM, LSTM-Network, and DeepAnt) is evaluated based on defined performance metrics. These methods are benchmarked on publicly available datasets, own synthetic datasets, and novel industrial datasets. The latter include radar sensor datasets from a hot rolling mill. Research results show a high detection performance of K-means algorithm, DBSCAN algorithm and LSTM network for punctual, collective and contextual anomalies. A decentralized strategy for (real-time) anomaly detection using sensor data streams is proposed and an industrial (Cloud-Edge Computing) platform is developed and implemented for this purpose.