A CNN-LSTM-based domain adaptation model for remaining useful life prediction. (1st November 2022)

• Record Type: Journal Article
• Authors: Liu, Huixiang; Chen, Wenbai; Chen, Weizhao; Gu, Yu
• Is Part Of: Measurement science & technology. Volume 33:Number 11(2022)
• Publication Date: 2022-11-01
• Subjects: domain adaptation -- remaining useful life prediction -- LSTM -- CNN -- neural network; Physical measurements -- Periodicals; Scientific apparatus and instruments -- Periodicals; Equipment and Supplies -- Periodicals; Science -- instrumentation -- Periodicals; Technology -- instrumentation -- Periodicals; Mesures physiques -- Périodiques; Physical measurements; Scientific apparatus and instruments; Periodicals; 502.87
• DOI: 10.1088/1361-6501/ac7f7f
• Languages: English
• ISSNs: 0957-0233

Abstract

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.