A Study of a Domain-Adaptive LSTM-DNN-Based Method for Remaining Useful Life Prediction of Planetary Gearbox

Abstract

Remaining Useful Life (RUL) prediction is an important component of failure prediction and health management (PHM). Current life prediction studies require large amounts of tagged training data assuming that the training data and the test data follow a similar distribution. However, the RUL-prediction data of the planetary gearbox, which works in different conditions, will lead to statistical differences in the data distribution. In addition, the RUL-prediction accuracy will be affected seriously. In this paper, a planetary transmission test system was built, and the domain adaptive model was used to Implement the transfer learning (TL) between the planetary transmission system in different working conditions. LSTM-DNN network was used in the data feature extraction and regression analysis. Finally, a domain-adaptive LSTM-DNN-based method for remaining useful life prediction of Planetary Transmission was proposed. The experimental results show that not only the impact of different operating conditions on statistical data was reduced effectively, but also the efficiency and accuracy of RUL prediction improved.