基于MsTCN-Transformer模型的轴承剩余使用寿命预测研究

邓飞跃1,陈哲1,郝如江1,杨绍普2

振动与冲击 ›› 2024, Vol. 43 ›› Issue (4) : 279-287.

PDF(2229 KB)
PDF(2229 KB)
振动与冲击 ›› 2024, Vol. 43 ›› Issue (4) : 279-287.
论文

基于MsTCN-Transformer模型的轴承剩余使用寿命预测研究

  • 邓飞跃1,陈哲1,郝如江1,杨绍普2
作者信息 +

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

  • DENG Feiyue1, CHEN Zhe1, HAO Rujiang1, YANG Shaopu2
Author information +
文章历史 +

摘要

剩余使用寿命(Remaining useful life, RUL)预测是PHM的核心问题之一,复杂的运行工况往往导致设备部件经历不同的故障退化过程,给RUL准确预测带来了巨大挑战。为此,本文提出了一种多尺度时间卷积网络(Multi-scale temporal convolutional network, MsTCN)与Transformer(MsTCN-Transformer)融合模型用于变工况下滚动轴承RUL预测。该方法设计了一种新的多尺度膨胀因果卷积单元(Multi-scale dilated causal convolution unit, MsDCCU),能够自适应地挖掘滚动轴承全寿命信号中固有的时序特征信息;然后构建了基于自注意力机制的Transformer网络模型,在克服预测序列记忆力退化的基础上,准确学习时序特征与轴承RUL之间的映射关系。此外,通过对轴承不同故障退化阶段所提取的时序特征可视化分析,验证了所提方法在变工况下所提取的时序特征泛化性较好。多种工况条件下滚动轴承RUL预测实验表明,所提方法能够较为准确地实现变工况下轴承的RUL预测,相比当前多种方法RUL预测结果准确性更高。

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.

关键词

剩余使用寿命 / 时序特征 / 时间卷积网络 / Transformer网络 / 滚动轴承

Key words

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

引用本文

导出引用
邓飞跃1,陈哲1,郝如江1,杨绍普2. 基于MsTCN-Transformer模型的轴承剩余使用寿命预测研究[J]. 振动与冲击, 2024, 43(4): 279-287
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

参考文献

[1] Deng Feiyue, Bi Yan, Liu Yongqiang, Yang Shaopu. Deep-Learning-Based Remaining Useful Life Prediction Based on a Multi-Scale Dilated Convolution Network. Mathematics, 2021, 9, 3035. [2] Wang B, Lei Y G, Yan T, et al. Recurrent convolutional neural network: A new framework for remaining useful life prediction of machinery[J]. Neurocomputing, 2020, 379:117-129. [3] 赵志宏, 李晴, 李乐豪,等. LSTM Encoder-Decoder方法预测设备剩余使用寿命[J]. 交通运输工程学报, 2021. 21(6):269-277. Zhao Zhihong, Li Qing, Li Lehao, et al. Remaining useful life prediction for equipment based on LSTM encoder-decoder method[J]. Journal of Traffic and Transportation Engineering. 2021. 21(6):269-277. [4] Benkedjouh T, Medjaher K, Zerhouni N, et al. Remaining useful life estimation based on nonlinear feature reduction and support vector regression[J]. Engineering Applications of Artificial Intelligence, 2013, 26(7): 1751-1760. [5] Gebraeel N, Lawley M, Liu R, Parmeshwaran V. Residual life predictions from vibration-based degradation signals: a neural network approach[J]. IEEE Trans Ind Electron 2004;51(3):694–700. [6] Moosavian A , Ahmadi H , Tabatabaeefar A , et al. An appropriate procedure for detection of journal-bearing fault using power spectral density, k-nearest neighbor and support vector machine[J]. International Journal on Smart Sensing & Intelligent Systems, 2017, 5(3):685-700. [7] Dong M, He D. A segmental hidden semi-Markov model (HSMM)-based diagnosticsand prognostics framework and methodology[J]. Mech Syst Signal Process, 2007, 21(5):2248–66. [8] L. Ren, Y. Sun, H. Wang, L. Zhang, Prediction of bearing remaining useful life with deep convolution neural network[J], IEEE Access, 6 (2018) 13041 -13049. [9] Wang B , Lei Y , Li N , et al. Deep separable convolutional network for remaining useful life prediction of machinery[J]. Mechanical systems and signal processing, 2019, 134:106330.1-106330.18. [10] Zhu J , Nan C , Peng W . Estimation of Bearing Remaining Useful Life based on Multiscale Convolutional Neural Network[J]. IEEE Transactions on Industrial Electronics, 2018, 66(4): 3208 – 3216. [11] 蒋全胜, 许伟洋, 朱俊俊, 等. 基于动态加权卷积长短时记忆网络的滚动轴承剩余寿命预测方法[J]. 振动与冲击, 2022, 41(17):282-291. Jiang Quangsheng, Xu Weiyang, Zhu Junjun, et al. Residual life prediction method of rolling bearing based on DW-CNN-LSTM networks[J]. Journal of vibration and shock. 2022, 41(17):282-291. [12] Ma M, Mao Z. Deep-convolution-based LSTM network for remaining useful life prediction[J]. IEEE Transactions on Industrial Informatics, 2020, 17(3): 1658-1667. [13] Luo J, Zhang X. Convolutional neural network based on attention mechanism and Bi-LSTM for bearing remaining life prediction[J]. Applied Intelligence, 2022, 52(1): 1076-1091. [14] Tian T , Song C , Jin T , et al. A French-to-English Machine Translation Model Using Transformer Network[J]. Procedia Computer Science, 2022, 199:1438-1443. [15] H Deguchi, Tamura A , Ninomiya T . Synchronous Syntactic Attention for Transformer Neural Machine Translation[C]// Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing: Student Research Workshop. 2021. [16] X. Li, W. Zhang, Q. Ding, Deep learning-based remaining useful life estimation of bearings using multi-scale feature extraction, Reliability Engineering & System Safety, 182 (2019) 208-218 [17] Jia F. J., Lei Y G, Lu N., et al. Deep normalized convolutional neural network for imbalanced fault classification of machinery and its understanding via visualization[J]. Syst. Sig. Process, 2018, 110:349-367. [18] Li H, Zhao W , Zhang Y , et al. Remaining useful life prediction using multi-scale deep convolutional neural network[J]. Applied Soft Computing, 2020, 89:106113. [19] 王久健, 杨绍普, 刘永强等. 一种基于空间卷积长短时记忆神经网络的轴承剩余寿命预测方法[J]. 机械工程学报, 2021, 57(21):88-95. Wang Jiujian, Yang Shaopu, Liu Yongqiang, et al. A Method of Bearing Remaining Useful Life Estimation Based on Convolutional Long Short-term Memory Neural Network[J]. Journal of Mechanical Engineering, 2021, 57(21):88-95. [20] Li X, Ding Q, Sun J Q. Remaining useful life estimation in prognostics using deep convolution neural networks[J]. Reliability Engineering & System Safety, 2018, 172: 1-11.

PDF(2229 KB)

Accesses

Citation

Detail

段落导航
相关文章

/