Change point detection and issue localization based on fleet-wide fault data

Abstract Modern industrial assets (e.g., generators, turbines, engines) are outfitted with numerous sensors to monitor key operating and environmental variables. Unusual sensor readings, such as high temperature, excessive vibration, or low current, could trigger rule-based actions (also known as faults) that range from warning alarms to immediate shutdown of the asset to prevent potential damage. In the case study of this article, a wind park experienced a sudden surge in vibration-induced shutdowns. We utilize fault data logs from the park with the goal of detecting common change points across turbines. Another important goal is the localization of fault occurrences to an identifiable set of turbines. The literature on change point detection and localization for multiple assets is highly sparse. Our technical development is based on the generalized linear modeling framework. We combine well-known solutions to change point detection for a single asset with a heuristics-based approach to identify a common change point(s) for multiple assets. The performance of the proposed detection and localization algorithms is evaluated through synthetic (Monte Carlo) fault data streams. Several novel performance metrics are defined to characterize different aspects of a change point detection algorithm for multiple assets. For the case study example, the proposed methodology identified the change point and the subset of affected turbines with a high degree of accuracy. The problem described here warrants further study to accommodate general fault distributions, change point detection algorithms, and very large fleet sizes.