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浙江大学学报(工学版)
Journal of ZheJiang University (Engineering Science)  2022, Vol. 56 Issue (7): 1447-1456    DOI: 10.3785/j.issn.1008-973X.2022.07.020
    
Bearing life prediction based on multi-scale features and attention mechanism
Ren-peng MO(),Xiao-sheng SI*(),Tian-mei LI,Xu ZHU
College of Missile Engineering, Rocket Force University of Engineering, Xi’an 710025, China
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Abstract  

A bearing RUL prediction method based on multi-scale features and attention mechanism was proposed aiming at the problem that the previous remaining useful life (RUL) prediction methods were insufficient in mining bearing degradation information and ignored the difference in the contribution of different features, which affected the prediction accuracy. Several time-domain and frequency-domain features of the original bearing vibration signal at multiple scales were calculated as the input feature set. The multi-scale feature set was input into the network, and the attention module was used to adaptively assign the best weights to different features. Then the convolutional neural network (CNN) module was used for deep feature extraction and multi-scale feature fusion. The RUL prediction value was obtained through the feedforward neural network (FNN) module mapping. The proposed method was applied to the public bearing datasets for comparative studies. Results showed the superior prediction performance of the proposed method.

Key words： remaining useful life prediction      multi-scale feature      attention mechanism      convolutional neural network (CNN)      bearing     
Received: 06 July 2021      Published: 26 July 2022
CLC:  TH 133  
Fund:  国家自然科学基金资助项目（61773386，62073336）；国家自然科学基金优秀青年资助项目（61922089）
Corresponding Authors: Xiao-sheng SI     E-mail: renpengmo@163.com;sxs09@mails.tsinghua.edu.cn
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Ren-peng MO
Xiao-sheng SI
Tian-mei LI
Xu ZHU
Cite this article:

Ren-peng MO,Xiao-sheng SI,Tian-mei LI,Xu ZHU. Bearing life prediction based on multi-scale features and attention mechanism. Journal of ZheJiang University (Engineering Science), 2022, 56(7): 1447-1456.

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https://www.zjujournals.com/eng/10.3785/j.issn.1008-973X.2022.07.020     OR     https://www.zjujournals.com/eng/Y2022/V56/I7/1447


基于多尺度特征与注意力机制的轴承寿命预测

针对以往剩余使用寿命（RUL）预测方法对轴承退化信息挖掘不充分、忽视不同特征贡献度差异，影响预测准确性的问题，提出基于多尺度特征与注意力机制的轴承RUL预测方法. 在多个尺度下计算轴承原始振动信号的若干时域和频域特征，作为输入特征集. 将多尺度特征集输入到网络中，以注意力模块为不同特征自适应地分配最佳权重，以卷积神经网络（CNN）模块进行深层特征提取与多尺度特征融合，通过前馈神经网络（FNN）模块映射得到RUL预测值. 通过公开的轴承数据集进行实验验证，与其他RUL预测方法相比，所提方法的预测性能更优越.


关键词： 剩余使用寿命预测,  多尺度特征,  注意力机制,  卷积神经网络(CNN),  轴承 
Fig.1 Procedure of remaining life prediction
Fig.2 Extraction of multi-scale feature
Fig.3 Schematic diagram of time window
Fig.4 Overall network structure
Fig.5 Attention mechanism
工况 nr/(r·min?1) F/N
1 1 800 4 000
2 1 650 4 200
3 1 500 5 000
Tab.1 Working condition details of experimental data
Fig.6 Vibration acceleration amplitudes of bearing full-life
网络模块 网络层 超参数设置
注意力模块 全连接层 U = K
CNN模块 卷积层1 n = 32，k = 6，s = 1
CNN模块 卷积层2 n = 64，k = 6，s = 1
CNN模块 卷积层3 n = 64，k = 6，s = 1
CNN模块 卷积层4 n = 64，k = 6，s = 1
FNN模块 隐藏层1 U = 100
FNN模块 dropout层 r = 0.2
FNN模块 隐藏层2 U = 20
FNN模块 输出层 U = 1
Tab.2 Network hyperparameters
Fig.7 RMSE and MAE under different time window sizes
评价指标 RMSE MAE
尺度1 0.034 0 0.029 5
尺度1+注意力 0.031 7 0.027 3
尺度2 0.032 2 0.028 5
尺度2+注意力 0.030 3 0.026 4
尺度4 0.027 4 0.023 4
尺度4+注意力 0.026 8 0.021 9
多尺度 0.024 4 0.019 9
多尺度+注意力 0.021 2 0.016 7
Tab.3 Evaluation metrics in ablation experiment
Fig.8 Comparison of evaluation metrics for ablation experiments
Fig.9 Prediction effect of different single-scale models
多尺度组合 RMSE MAE ttr/s tte/s
1-2-4 0.021 2 0.016 7 4.64 2.73
1-2-4-8 0.022 6 0.019 3 7.15 3.99
1-2-4-8-16 0.021 1 0.017 4 11.20 5.20
Tab.4 Prediction effect of various multi-scale combinations
网络 RMSE MAE ttr/s tte/s
DNN 0.065 6 0.057 7 1.32 0.91
CNN 0.030 7 0.026 0 5.05 2.47
MSCNN 0.023 1 0.018 6 52.00 8.69
BiLSTM 0.058 4 0.045 1 4.49 11.45
CNN-LSTM 0.056 0 0.046 2 51.00 44.48
本文方法 0.021 2 0.016 7 4.64 2.73
Tab.5 Evaluation metrics of various methods
Fig.10 Comparison of prediction results of various methods
网络 轴承2-3 轴承3-1
RMSE MAE RMSE MAE
DNN 0.360 7 0.330 0 0.348 9 0.322 6
CNN 0.118 4 0.103 8 0.341 7 0.334 7
BiLSTM 0.165 8 0.135 9 0.229 4 0.210 1
本文方法 0.044 1 0.034 6 0.075 8 0.061 8
原训练集 0.448 6 0.426 8 0.438 9 0.420 9
Tab.6 Prediction performance of bearings under different operating conditions
Fig.11 RUL prediction curve of bearing under different operating conditions
网络 轴承1-1 轴承2-2 轴承3-2
RMSE MAE RMSE MAE RMSE MAE
DNN 0.205 2 0.176 9 0.249 1 0.206 1 0.303 2 0.246 7
CNN 0.153 4 0.125 3 0.113 4 0.088 7 0.228 7 0.203 9
BiLSTM 0.144 8 0.123 5 0.102 6 0.088 7 0.207 9 0.164 9
本文方法 0.098 6 0.083 6 0.054 8 0.045 3 0.104 4 0.077 6
Tab.7 Prediction effect of different methods in XJTU-SY bearing data set
Fig.12 RUL prediction curve of test-set bearings (XJTU-SY)
Fig.13 RUL prediction uncertainty measurement of bearing 1-3 (FEMTO-st)
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