A machine learning-based workflow for automatic detection of anomalies in machine tools. (June 2022)

• Record Type: Journal Article
• Authors: Züfle, Marwin; Moog, Felix; Lesch, Veronika; Krupitzer, Christian; Kounev, Samuel
• Is Part Of: ISA transactions. Volume 125(2022)
• Page Start: 445
• Page End: 458
• Publication Date: 2022-06
• Subjects: Predictive maintenance -- Industry 4.0 -- Industrial Internet-of-Things -- Clustering -- Anomaly detection; Engineering instruments -- Periodicals; Engineering instruments; Periodicals; Electronic journals; 629.805
• DOI: 10.1016/j.isatra.2021.07.010
• Languages: English
• ISSNs: 0019-0578

Abstract

Abstract: Despite the increased sensor-based data collection in Industry 4.0, the practical use of this data is still in its infancy. In contrast, academic literature provides several approaches to detect machine failures but, in most cases, relies on simulations and vast amounts of training data. Since it is often not practical to collect such amounts of data in an industrial context, we propose an approach to detect the current production mode and machine degradation states on a comparably small data set. Our approach integrates domain knowledge about manufacturing systems into a highly generalizable end-to-end workflow ranging from raw data processing, phase segmentation, data resampling, and feature extraction to machine tool anomaly detection. The workflow applies unsupervised clustering techniques to identify the current production mode and supervised classification models for detecting the present degradation. A resampling strategy and classical machine learning models enable the workflow to handle small data sets and distinguish between normal and abnormal machine tool behavior. To the best of our knowledge, there exists no such end-to-end workflow in the literature that uses the entire machine signal as input to identify anomalies for individual tools. Our evaluation with data from a real multi-purpose machine shows that the proposed workflow detects anomalies with an average F1-score of almost 93%. Highlights: Machine learning-based, autonomic detection of anomalies using real production data. Black-box approach without requiring expert knowledge or specialized hardware. Good detection of anomalies in machine tools with only few training data. Random forests detect anomalies with an F1-score and accuracy of up to 91% each. Individual decision trees can even surpass random forest for a high rotational speed.