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.
Highlights
IntroductionThere has been an increased interest in analysis tools for industrial applications
In recent years, there has been an increased interest in analysis tools for industrial applications
The output of the algorithm are two meta-time series, namely the matrix profile (MP), which contains the distance to the nearest neighbor, and the profile index (PI), which contains the position of the nearest neighbor
Summary
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. Predictive maintenance (PdM) focuses on diagnosing the machine status and providing insights about its current and future conditions. 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]. This work focuses on analyzing the signal under real operation conditions, which poses
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