Fault Knowledge Transfer Assisted Ensemble Method for Remaining Useful Life Prediction

Abstract

Machinery remaining useful life (RUL) prediction is an important task in condition-based maintenance. Data-driven methods have been widely studied and applied, however, almost all the researches learn degradation trends regardless of different fault conditions, which can lead to different degradation patterns. This article proposes a novel fault information assisted RUL prediction method based on a convolutional long short-term memory (LSTM) ensemble network, where fault conditions are obtained via fault knowledge transfer. Divergence minimization and domain adversarial adaptation are combined to transfer fault knowledge from a fault dataset to the run-to-failure data in a weakly supervised manner. With the predicted fault information, the RUL prediction network can learn various degradation patterns under different faults separately using a structure of multiple LSTMs. Then an ensemble strategy based on <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">soft fault conditions</i> is designed to get final RUL prediction results. Experiment on bearing datasets verifies the effectiveness of our proposed method.