A Relative Entropy Weibull-SAX framework for health indices construction and health stage division in degradation modeling of multivariate time series asset data

Abstract

Predictive maintenance is the monitoring of an asset’s condition over its life cycle to provide a prognosis for when maintenance is required. Prior to prognosis, an asset’s life cycle is modeled by a health indicator (HI) which is derived from sensor measurements and describes an asset’s degradation over a life cycle. Often an asset’s HI is accompanied by a health stage (HS) division, which describes the asset’s life cycle condition in discrete states. Generally, HSs are discrete representations generated from discrete state transition models, dynamic state space models, or subjectively defined thresholds, which use sensor measurements to provide a HS division related to an asset’s life cycle degradation. HS division methods are often designed for a specific asset in which HS division is based on user assumptions and not generalizable to different asset types or asset data representations. Also, HS division methods are often limited to a bi-state HS division (normal and failure), in which unobservable states are often generalized transition states. As assets become more complex and require multivariate measurements, more advanced methods are required to model an asset’s degradation using HIs and HSs. This work introduces Relative Entropy Weibull-SAX (REWS), a data-driven HI and HS degradation modeling method for multivariate asset data. REWS constructs a HI using relative entropy to represent an asset’s condition as a change of entropy during its life cycle. The relative entropy representation is then discretized into HS divisions using a Weibull distribution based Symbolic Aggregate approXimation. REWS’s utility is demonstrated on the Commercial Modular Aero-Propulsion System Simulation dataset which describes the life cycle observations of multiple aircraft engines.