A Functional Analysis Approach for Prototyping a Prognostic Model

Abstract

Large datasets in prognostics and health management (PHM) can often present challenges for degradation prediction models because of size and computational cost. In this work, a methodology is introduced that employs functional data analysis (FDA) to compress high dimensional sensor data using a B-spline basis. This method was able to reduce the dimensionality of the data while preserving the essential characteristics of the underlying trend. The reduced dimensional representation serves as input to a k-Nearest Neighbors (KNN) algorithm for remaining useful life (RUL) estimation of a jet engine nacelle fan. Experimental results demonstrate that the functional KNN (fKNN) framework can predict part failure within 50 flight hours, achieving sufficiently reliable and precise RUL forecasts on the test data set. The optimal hyperparameters as validated by the test set used 25 nearest neighbors, a prediction count of 3, and a prediction minimum threshold of 0.44. Therefore, the combined use of FDA for dimensionality reduction and KNN for RUL prediction offers a scalable solution for handling big data (~20 GB) PHM challenges without sacrificing predictive performance.