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A Concise Self-adapting Deep Learning Network for Machine Remaining Useful Life Prediction by Sheng Xiang; Yi Qin; Jun Luo; Fei Wu; Konstantinos Gryllias is a Engineering article available to read on EtoBox.

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Most remaining useful life (RUL) prediction methods learn the feature using a single fixed pattern, resulting in a lack of self-adapting learning capability and a decrease in generalization and prediction accuracy. To address this issue, we propose a concise self-adapting deep learning network (CSDLN) for RUL prediction with fewer learnable parameters per sub-module. First, a multi-branch 1D involution neural network (MINN) is proposed to adaptively extract the hidden feature from the multi-input using the involution operation, which has inverse inherence with the convolution operation. Second, an adaptive learning algorithm called the multi-head gated recurrent unit (MGRU) is proposed to learn the hidden feature. Finally, the aero-engine RUL is determined by dimension reduction of the full connection (FC) layer and linear regression of the regression layer. Additionally, rather than using the ReLU activation function, the Mish activation function is used to strengthen the self-adapting deep learning ability in CSDLN. The performance in the Commercial Modular Aero Propulsion System Simulation (C-MAPSS) dataset and the real wind turbine gearbox bearing tests demonstrate the superior

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Author

Sheng Xiang; Yi Qin; Jun Luo; Fei Wu; Konstantinos Gryllias

Publisher

Elsevier BV

Published

2023

Language

EN

Field

Engineering (Physical Sciences)