Remaining Useful Life Prediction of Electromechanical Equipment based on Particle Filter and LSTM

Ning Yang, Chunyue Gu, Yanping Huang, Pengfei Wen, Shuai Zhao, Shuaiwen Feng, Shaowei Chen

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1 Citationer (Scopus)

Abstract

The Long Short Term Memory (LSTM) neural network can reach high prediction accuracy when analyzing industrial equipment signals, so it is widely used in Remaining Useful Life (RUL) prediction of industrial equipment. However, there are still several challenges in training LSTM networks, such as prone converging to a local optimal solution, weak generalization ability, and inability to provide uncertainty of estimated RUL, which make it difficult to apply in practice. Aiming at the existing problems, this paper proposes an RUL prediction algorithm based on the model fusion of Particle Filter (PF) and LSTM. The re-sampling process of the PF is improved based on the weight division and the neighboring combination. An LSTM network is deployed as the state transition equation of the PF. The signal noise is extracted and reconstructed based on the wavelet transform to create the particle set. The improved PF algorithm is used to optimize the training of the LSTM to search the global optimal solution. The weight coefficients of the PF are used to generate the CI (CI) of the RUL. The experimental verification on NASA Electromechanical Actuators (EMAs) data set shows that the proposed fusion model reaches higher accuracy and reliability.

OriginalsprogEngelsk
TitelProceedings of 2022 IEEE International Conference on Sensing, Diagnostics, Prognostics, and Control, SDPC 2022
RedaktørerQibing Yu, Diego Cabrera, Jiufei Luo, Zhiqiang Pu
Antal sider5
ForlagIEEE Signal Processing Society
Publikationsdato2022
Sider87-91
ISBN (Elektronisk)9781665469869
DOI
StatusUdgivet - 2022
Begivenhed6th IEEE International Conference on Sensing, Diagnostics, Prognostics, and Control, SDPC 2022 - Chongqing, Kina
Varighed: 5 aug. 20227 aug. 2022

Konference

Konference6th IEEE International Conference on Sensing, Diagnostics, Prognostics, and Control, SDPC 2022
Land/OmrådeKina
ByChongqing
Periode05/08/202207/08/2022
SponsorChina Electronic Product Reliability and Environmental Testing Research Institute (CEPREI), Chongqing Jiaotong University, Chongqing University of Posts and Telecommunications, et al., Hefei Institutes of Physical Science of Chineses Academy of Sciences, IEEE Beijing Section
NavnProceedings of 2022 IEEE International Conference on Sensing, Diagnostics, Prognostics, and Control, SDPC 2022

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