Deep sequence modeling for event log-based predictive maintenance

Predictive maintenance (PdM) is one of the most important machine learning (ML) use cases in a wide range of businesses that own or manufacture machinery. Many PdM applications utilize event logs/sequences that record machine operating conditions and health data. However, industrial scale PdM use cases bring a variety of challenges which makes it difficult to directly apply state-of-the-art models. In this work, we start with a PdM use case from a company Light & Wonder (L&W) Inc., which requires us to work with about 280 million events from ∼500 gaming machines accumulated over 10 months, and a short 24-hour time window could contain from 0 to approximately 29,000 events. Developing performant models for event sequence data at such scale is a non-trivial task. In this work, we propose a generic, generalizable modeling framework which we refer to as ElasticPdM for industrial scale PdM applications using event log data, which uses novel methods including time-adaptive event sequence binning and time-aware event sequence embedding to process long event sequences. Applied on the real-world L&W data, our ElasticPdM is 13-20% more accurate than state-of-the-art baselines on Recall at High Precision metrics used commonly for PdM.