Integrating Feature Engineering with Deep Learning to Conduct Diagnostic and Predictive Analytics for Turbofan Engines

Abstract

The prediction of remaining useful life (RUL) is a critical issue in many areas, such as aircrafts, ships, automobile, and facility equipment. Although numerous methods have been presented to address this issue, most of them do not consider the impacts of feature engineering. Typical techniques include the wrapper approach (using metaheuristics), the embedded approach (using machine learning), and the extraction approach (using component analysis). For simplicity, this research considers feature selection and feature extraction. In particular, principal component analysis (PCA) and sliced inverse regression (SIR) are adopted in feature extraction while stepwise regression (SR), multivariate adaptive regression splines (MARS), random forest (RF), and extreme gradient boosting (XGB) are used in feature selection. In feature selection, the original 15 sensors can be reduced to only four sensors that accumulate more than 80% degrees of importance and not seriously decrease the predictive performances. In feature extraction, only the top three principal components can account for more than 80% variances of original 15 sensors. Further, PCA combined with RF is more recommended than PCA and CNN (convolutional neural network) because it can achieve satisfactory performances without incurring tedious computation.