Unsupervised Domain Adaptation based Remaining Useful Life Prediction of Rolling Element Bearings

Abstract

With the rise of Artificial Intelligence (AI), machine learning techniques are now conquering the research field of Prognostics and Health Management (PHM). Classic deployable prognostic models manipulate large amount of machinery historical data to map the degradation process based on inherent features. Nowadays one of the major challenges in prognostics research is the data deficit problem when historical data is not available or accessible, in enough quantity and variety. In the frames of Transfer Learning, the domain adaptation technique aims to build a model with strong generalization ability which can be transferred to datasets with different distributions. In this paper, a Domain Adversarial Neural Network (DANN) model is combined with a Bidirectional Long Short- Term Memory (Bi-LSTM) neural network for the estimation of the Remaining Useful Life (RUL) of rolling element bearings. The unsupervised domain adaptation is fulfilled using a labelled bearing degradation dataset as the source domain data and an unlabelled dataset captured under different operation conditions as the target domain data for the Bi-LSTM DANN. The proposed method achieves promising results, applied on real bearing vibration data captured on run-to-failure tests, with high prediction accuracy of the bearing RUL compared to un-adapted methods.