Bayesian hierarchical models for aerospace gas turbine engine prognostics

Prognostics is an emerging requirement of modern health monitoring that aims to increase the fidelity of failure-time predictions by the appropriate use of sensory and reliability information. In the aerospace industry, it is a key technology to maximise aircraft availability, offering a route to increase time in-service and to reduce operational disruption through improved asset management. An aircraft engine is a complex system comprising multiple subsystems that have dependent interactions. Therefore, it is difficult to construct a physics-based approach that mimics the dynamics of the system's degradation. This complexity indicates a statistically robust methodology for handling the large quantities of data, received in real-time from modern gas turbines. In this work, a Bayesian hierarchical model is selected to exploit fleet-wide data from multiple assets to perform probabilistic estimation of remaining useful life for civil aerospace gas turbine engines. The paper presents two Bayesian approaches distinguished by the modelling level, namely an existing Bayesian non-Hierarchical Model and the proposed Bayesian Hierarchical Model. The techniques use Bayesian method to combine two sources of information: historical in-service data across the engine fleet and once per-flight transmitted performance measurement from the engine(s) under prognosis. The proposed technique provides predictive results within well defined uncertainty bounds and demonstrates several advantages of the hierarchical variant's ability to integrate multiple unit data to address realistic prognostic challenges. This is illustrated by an example from civil aerospace gas turbine fleet data.