Research on bearing remaining useful life prediction based on an MsTCN-Transformer model

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Journal of Vibration and Shock ›› 2024, Vol. 43 ›› Issue (4) : 279-287.

DENG Feiyue1, CHEN Zhe1, HAO Rujiang1, YANG Shaopu2

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Abstract

Remaining useful life (RUL) prediction is one of the key issues to be solved in prognostics and health management (PHM). Equipment components suffer from different failure degradation processes due to the influence of complex operating conditions, which poses a great challenge for accurate RUL prediction. This paper proposes a novel joint mode of multi-scale temporal convolutional network (MsTCN) and Transformer network (MsTCN-Transformer) for RUL prediction of rolling bearings under variable operating conditions. The method presents a new multi-scale dilated causal convolution unit (MsDCCU) that can adaptively mine the time-sequence feature information inherent in the whole-life signals of bearings. Then a Transformer network based on the self-attentive mechanism is constructed to accurately learn the mapping relationship between the time-sequence features and the bearing RUL by overcoming the memory degradation of the prediction sequence. In addition, the visual analysis for different stages of bearing failure degradation verifies that the proposed method has a better generalization performance of the extracted time-sequence features. The experiments show that the proposed method can achieve more accurate RUL prediction, and the accuracy of RUL prediction is higher compared with other current related methods.

Key words

remaining useful life / time-sequence feature / temporal convolutional network / Transformer network / rolling element bearing

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DENG Feiyue1, CHEN Zhe1, HAO Rujiang1, YANG Shaopu2. Research on bearing remaining useful life prediction based on an MsTCN-Transformer model[J]. Journal of Vibration and Shock, 2024, 43(4): 279-287

References

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