We deal with a real predictive maintenance (PdM) scenario in an Industry 4.0 setting. With a help of the Sibyl platform, we can monitor live data from key components of a Philips factory equipment; in this work, we focus on a cold-forming press. Due to the dynamic environment of the operation of this press, unsupervised anomaly detection techniques are used to timely detect the wear, where early anomalies are interpreted as warning signs of a forthcoming failure. Typically such techniques assign an anomaly score, and the problem we face is how to appropriately set a threshold for this score. We introduce and compare four generally applicable thresholding techniques, two of which are dynamic, i.e., they continuously refine the threshold during the episode lifetime. We discuss the properties of these techniques and quantitatively evaluate their behavior in our case study.
We deal with the problem of predictive maintenance (PdM) in a vehicle fleet management setting following an unsupervised streaming anomaly detection approach. We investigate a variety of unsupervised methods for anomaly detection, such as proximity-based, hybrid (statistical and proximity-based) and transformers. The proposed methods can properly model the context in which each member of the fleet operates. In our case, the context is both crucial for effective anomaly detection and volatile, which calls for streaming solutions that take into account only the recent values. We propose two novel techniques, a 2-stage proximity-based one and context-aware transformers along with advanced thresholding. In addition, to allow for testing PdM techniques for vehicle fleets in a fair and reproducible manner, we build a new fleet-like benchmarking dataset based on an existing dataset of turbofan simulations. Our evaluation results show that our proposals reduce the maintenance costs compared to existing solutions.
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