Continuous Health Monitoring of Machinery using Online Clustering on Unlabeled Data Streams

Maintenance is an important support function to ensure the reliability, safety, and availability in the railway. Lately, machine learning has become a major player and allows practitioners to build intricate learning models for machinery maintenance. Commonly, a model is trained on static data and is retrained on new data that exhibit novelties unknown to the model. On the contrary, online machine learning is a learning paradigm that adapts the models to new data, thus enabling adaptive, lifelong learning. Our goal is to leverage online learning on unlabeled data streams to enhance railway machinery maintenance. We propose Continuous Health Monitoring using Online Clustering (CheMoc) as an unsupervised method that learns the health profiles of the systems incrementally, assesses their working condition continuously via an adaptive health score, and works efficiently on streaming data. We evaluate CheMoc on a real-world data set from a national railway company. The results show that CheMoc discovered relevant health clusters, as confirmed by a domain expert, and processed the data of an entire year under two hours using only 600 MB of memory.