Log-Based Predictive Maintenance

Predictive maintenance strives to anticipate equipment failures to allow for advance scheduling of corrective maintenance, thereby preventing unexpected equipment downtime and improving service quality for the customers. We present a data-driven approach based on multiple-instance learning for predicting equipment failures by mining equipment event logs which, while usually not designed for predicting failures, contain rich operational information. We discuss problem domain and formulation, evaluation metrics and predictive maintenance work flow. We experimentally compare our approach to competing methods. For evaluation, we use real life datasets with billions of log messages from two large fleets of medical equipment. We share insights gained from mining such data. Our predictive maintenance approach, deployed by a major medical device provider over the past several months, learns and evaluates predictive models from terabytes of log data, and actively monitors thousands of medical scanners.