GFRBS-PHM: A Genetic Fuzzy Rule-Based System for PHM with improved interpretability

Abstract

This paper presents an approach to predict the Remaining Useful Life (RUL) of a generic system when a higher level of interpretability of the prediction model is desired. A set of well known computational intelligence techniques such as Decision Trees, Fuzzy Logic, and Genetic Algorithms is used to generate a hybrid model which is called Genetic Fuzzy Rule-Based System (GFRBS) supported by a Decision Tree. The proposed method automatically generates fuzzy rules and tunes the associated membership functions. Accuracy and improved interpretability are achieved during training since they are coded in the fitness function used by the genetic algorithm. The proposed approach is applied to a case study of degradation of aeronautical engines. The task is to estimate the remaining useful life of a commercial aircraft engine using only historical data gathered by the sensors embedded in the engine.