A RUL Prediction Method of Small Sample Equipment Based on DCNN-BiLSTM and Domain Adaptation

Wenbai Chen, Weizhao Chen, Huixiang Liu, Yiqun Wang, Chunli Bi and Yu Gu
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Wenbai Chen: School of Automation, Beijing Information Science and Technology University, Beijing 100101, China
Weizhao Chen: School of Automation, Beijing Information Science and Technology University, Beijing 100101, China
Huixiang Liu: School of Automation, Beijing Information Science and Technology University, Beijing 100101, China
Yiqun Wang: School of Automation, Beijing Information Science and Technology University, Beijing 100101, China
Chunli Bi: China Academy of Information and Communications Technology, Beijing 100191, China
Yu Gu: Guangdong Province Key Laboratory of Petrochemical Equipment Fault Diagnosis, Guangdong University of Petrochemical Technology, Maoming 525000, China

Mathematics, 2022, vol. 10, issue 7, 1-14

Abstract: To solve the problem of low accuracy of remaining useful life (RUL) prediction caused by insufficient sample data of equipment under complex operating conditions, an RUL prediction method of small sample equipment based on a deep convolutional neural network—bidirectional long short-term memory network (DCNN-BiLSTM) and domain adaptation is proposed. Firstly, in order to extract the common features of the equipment under the condition of sufficient samples, a network model that combines the deep convolutional neural network (DCNN) and the bidirectional long short-term memory network (BiLSTM) was used to train the source domain and target domain data simultaneously. The Maximum Mean Discrepancy (MMD) was used to constrain the distribution difference and achieve adaptive matching and feature alignment between the target domain samples and the source domain samples. After obtaining the pre-trained model, fine-tuning was used to transfer the network structure and parameters of the pre-trained model to the target domain for training, perform network optimization and finally obtain an RUL prediction model that was more suitable for the target domain data. The method was validated on a simulation dataset of commercial modular aero-propulsion provided by NASA, and the experimental results show that the method improves the prediction accuracy and generalization ability of equipment RUL under cross-working conditions and small sample conditions.

Keywords: DCNN-BiLSTM; domain adaptation; MMD; fine-tuning; C-MAPSS; cross-working; small sample (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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