Pseudolabeling Machine Learning Algorithm for Predictive Maintenance of Relays

Abstract

Predictive maintenance (PdM) has become an important industrial feature. Existing methods mainly focus on remaining useful life (RUL) regression or anomaly detection to achieve PdM in a given application. Those approaches assume monotonic degradation processes leading to a single catastrophic failure at the system's end of lifetime. In contrast, much more complex degradation processes can be found in real-world applications, which are characterized by effects like self-healing or noncatastrophic anomalies. A important example of devices with complex degradation are electromechanical relays. As established PdM solutions failed when applied to a real-world relays degradation data set, the maintenance algorithm for unlabeled data (MAUD) is presented to detect signs of wear and enable a service in time. In detail, MAUD is based on an artificial neural network (ANN), which is trained semisupervised. Experiments with measurement data from 546 relays show that MAUD is superior to various existing methods: The static B10 threshold, which represents the state of the art in relay maintenance, is surpassed by a 17.07 p.p. increase in utilization while reducing failures by 6.42 p.p. Methods based on machine learning, such as RUL estimation and anomaly detection, achieved much lower utilization (up to 31.83 p.p.) compared with MAUD while maintaining the same failure rate.