Anomaly detection and event mining in cold forming manufacturing processes
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ORIGINAL ARTICLE
Anomaly detection and event mining in cold forming manufacturing processes Diego Nieves Avendano1
· Daniel Caljouw2 · Dirk Deschrijver1 · Sofie Van Hoecke1
Received: 10 August 2020 / Accepted: 24 September 2020 © The Author(s) 2020
Abstract Predictive maintenance is one of the main goals within the Industry 4.0 trend. Advances in data-driven techniques offer new opportunities in terms of cost reduction, improved quality control, and increased work safety. This work brings data-driven techniques for two predictive maintenance tasks: anomaly detection and event prediction, applied in the real-world use case of a cold forming manufacturing line for consumer lifestyle products by using acoustic emissions sensors in proximity of the dies of the press module. The proposed models are robust and able to cope with problems such as noise, missing values, and irregular sampling. The detected anomalies are investigated by experts and confirmed to correspond to deviations in the normal operation of the machine. Moreover, we are able to find patterns which are related to the events of interest. Keywords Predictive maintenance · Anomaly detection · Association rule mining · Multivariate data · Matrix profile
1 Introduction In recent years, there has been an increased interest in analysis tools for industrial applications. Within Industry 4.0, one of the goals is combining sensor technologies with data analysis tools in order to improve the manufacturing process. In general, predictive maintenance (PdM) focuses on diagnosing the machine status and providing insights about its current and future conditions. In this paper, we analyze two key aspects of PdM: online anomaly detection (AD) and event prediction on a cold forming manufacturing line. The cold forming line is equipped with acoustic emission sensors (AE) that provide high-frequency information about the mechanical conditions of the press components. This type of sensors has previously been used to investigate failure modes of mechanical components under laboratory settings [4, 7]. In contrast, this work focuses on analyzing the signal under real operation conditions, which poses Diego Nieves Avendano
[email protected] Daniel Caljouw [email protected] 1
IDLab, Ghent University - imec, Ghent, Belgium
2
Philips Consumer Lifestyle B.V., Eindhoven, Netherlands
challenges in the data quality and model capabilities. In particular, the data is challenging as samples are discontinuous and sampled at irregular intervals. This, in turn, prevents the usage of standard time series analysis approaches. Other problems which are also addressed include presence of noise, sensors getting disconnected, logging errors, and constant stops and restarts of the machines due to production bottlenecks. Anomalies are events that do not conform to the normal or expected behavior of a process. Their correct detection is useful as it can uncover events of interest. In the manufacturing industry, AD provides benefits in terms of quality control, safety, an
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