Explainable Prognostics Method through Differential Evolved RVR Ensemble of Relevance Vector Machines

Abstract

Operating experience from various mechanical components indicates that their operating performance depends on non-well known physical mechanisms, while it is likely that  various unexpected factors will act as catalysts for reaching the failure point. Therefore, one way to overcome the partially knowledge of physical mechanisms is the use of data-driven methods that estimate the degradation patterns and predict the failure point. Thus, there is a growing need to design and develop new and more sophisticated prognostic technologies that can estimate the remaining useful life of a mechanical component. In this work, a new method for prognostics is proposed that not only provides a prediction over the failure point but also provides an explanation over the rationale behind that prediction. The proposed method utilized tools from artificial intelligence and more specifically relevance vector machines (RVM) and differential evolution (DE). The cornerstone of the method is the assembly of an ensemble comprised of multiple RVM equipped with different kernels, and the subsequent evolution of the ensemble using the differential evolution. DE will provide a set of values for the coefficients of the ensemble. Then based on the coefficients together with their associated RVMs are used to provide an explanation over the prediction. The explanation stems from the kernels themselves as each kernel models different set of properties. The presented method is tested on a set of real-world degradation data taken from a Gas Turbine (GT) propulsion plant.