A CNN-LSTM-based domain adaptation model for remaining useful life prediction

Abstract Remaining useful life (RUL) estimation is fundamental to prediction and health management technology. Traditional machine learning generally assumes that the training and testing sets are independent and identically distributed. As distribution differences exist in real scenarios, this assumption hinders the effectiveness of the traditional machine learning methods. Aiming at these problems, we propose a CNN-LSTM-based domain adaptation framework for RUL prediction in this work. A shared encoding network and domain adaptation mechanism is introduced to decrease the data distribution discrepancy between the source and target domains. A cross-linking architecture is also developed for feature fusion, which considers the features at different levels to guarantee that the generated fusion features contain sufficient information for prognosis. Extensive experiments are then conducted to verify the superiority of the proposed framework. The experimental results demonstrate that the proposed model has excellent performance, especially for equipment with more complex working conditions and data.