Turbofan Engine RUL Prediction using ICA and Machine Learning Algorithms

Abstract

This paper takes an approach to the determination on the Remaining Useful Life (RUL) on a real-life turbo engine model selected from a set of data provided within the public domain for research purposes from the Prognostics Data Repository of NASA. The RUL analysis algorithm uses Independent Component Analysis for data dimensionality reduction and data processing simplicity due to the large number of involved sensors, then a model is trained to predict the remaining useful life for the turbo engine using Random Forest (RF) and Gradient Boosted Machine Algorithms (GBMA). The final RUL data is compared to the real RUL vs. time provided within the original data date for algorithm validation.