A Noise-Boosted Remaining Useful Life Prediction Method for Rotating Machines Under Different Conditions

Abstract

Remaining useful life (RUL) prediction methods for rotating machines have been successfully developed in recent decades. More attention should be paid to predictions with inconsistent data distributions under different conditions. To solve this problem, this article proposes a new RUL prediction method that includes two phases. In the first phase, degradation features are extracted from both the training and testing data sets using probabilistic principal component analysis (PPCA). In the second phase, additive white Gaussian noise (AWGN) is intentionally injected into the degradation features; thereafter, the features that are mixed with manually injected noise are imported into a bidirectional long short-term memory (BLSTM) network. The AWGN can enhance the robustness of the RUL prediction method and achieve prediction for machines under different conditions. In contrast to most deep learning-based RUL prediction methods, the training samples are intentionally “polluted” by manually injected noise. The effectiveness of the proposed method is validated using the C-MAPSS lifetime data set for aeroengines and compared with the effectiveness of state-of-the-art approaches.