Data Prognostics Using Symbolic Regression

Abstract

This paper describes a general technique for data prognostics using symbolic regression. This analysis treats the characterization of turbofan engine degradation as a particular application for the general technique. The proposed genetic program (GP) characterizes engine degradation, and then uses that characterization to both detect engine faults and predict the remaining lifetimes of engines after a fault. The genetic program exploits the fact that engine degradation manifests itself as changing correlations between sensor outputs. The NASA Prognostics Data Repository provides a training set in which 100 simulated engines are run to failure, and a test set in which a separate set of 100 simulated engines are shut off before they fail. The GP uses the training fleet of engines to identify the sensor relationships that indicate engine fault and predict remaining lifetime, and then observes the learned sensor relationships in the test fleet. The genetic program successfully detects the moment that the fault occurs for every engine in the test fleet and accurately predicts the remaining lifetime of the engines after the fault.