Bearing fault size estimation based on convolutional bidirectional long and short term memory networks
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摘要:
基于振动监测数据的航空发动机滚动轴承损伤大小识别，对于研究滚动轴承故障演化、故障预测和故障诊断具有重要意义。针对传统模型对先验知识依赖性高、特征提取不充分、故障尺寸训练类别有限等问题，提出了一种基于深度学习的滚动轴承损伤尺寸预计方法，能够对训练过程中未出现的中间尺寸进行准确识别。在经典模型的基础上，搭建了一种深度卷积网络与长短期记忆网络组合模型，该模型可对轴承振动信号的多维特征与时序特征进行充分提取，实现轴承故障的智能和高效诊断。最后，利用滚动轴承加速疲劳试验机，进行了多种转速与损伤尺寸下的滚动轴承故障试验，基于试验数据进行了方法的比较，结果表明，该组合网络的在正常和加噪的情况下预测精度分别达到99.94%和98.67%，较单独的深度卷积网络、长短期记忆网络及其他模型精度更高，比较结果充分表明了本文所提方法的优越性。
Abstract:The damage size identification of aero-engine rolling bearing based on vibration monitoring data is of great significance to the study of rolling bearing fault evolution, prediction and diagnosis. In view of inherent restrictions in traditional identification models such as high dependence on prior knowledge, insufficient feature extraction and limited category of training fault sizes, a prediction method of rolling bearing damage size based on deep learning was proposed, which can accurately identify the middle sizes that did not appear in the training process. A combined model of deep convolutional long-short-term memory network was developed, which can sufficiently extract the multi-dimensional and time-series characteristics of bearing vibration signal, and realize the intelligent and efficient diagnosis of bearing fault. On the basis of theoretical analysis, the rolling bearing fault tests under various damage sizes and rotational velocities were carried out by using the accelerated fatigue testing machine for rolling bearings, and the traditional and novel methods were compared based on the test data. The results showed that the prediction accuracy of the combined network can reach 99.94% and 98.67%, respectively, under normal and noisy conditions, higher than the single deep convolution network, long-short-term memory network and other models. The comparison results amply demonstrate the superiority of the proposed method.
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图 8 滚动轴承9种损伤尺寸时域波形图
Figure 8. Time domain waveform of 9 fault sizes of rolling bearings
图 9 滚动体进入至离开剥落区所产生的振动响应
Figure 9. Vibration response of rolling element entering and leaving fault zone
图 13 深度卷积神经网络（DCNN）预测结果
Figure 13. Predicted results of deep convolution neural network （DCNN）
图 15 添加高斯白噪声前后的信号时域波形与矩阵图
Figure 15. Time domain waveform and matrix diagram of signal before and after adding white Gaussian noise
表 1 不同优化器的训练时长与损失对比
Table 1. Training duration and verification loss of different optimizers
优化器 批量大小 最终验证损失 训练耗时/s SGDM 32 0.0094 426 64 0.0022 275 128 0.0061 192 ADAM 32 0.0261 583 64 0.0193 404 128 0.0270 239 RMSProp 32 0.0221 624 64 0.0272 621 128 0.0134 507 
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表 2 HRB 6206深沟球轴承的主要参数
Table 2. Main parameters of HRB 6206 deep groove ball bearing
内径/
mm外径/
mm厚度/
mm滚珠直径/
mm节径/
mm滚珠数 接触角/
（°）30 62 16 9.5 46 9 0
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表 3 数据采集的具体方案
Table 3. Specific scheme of data acquisition
项目 对应参数 采样频率/kHz 128 转速/（r/min） 2400 训练尺寸/mm 0.8, 1.0, 1.2, 1.6, 2.0, 2.2, 2.4 预测中间尺寸/mm 1.4, 1.8 采样时间/s 1 数据点数 131072 样本数 每种故障50个
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表 4 1.4 mm与1.8 mm损伤尺寸估计结果对比（n=2400 r/min）
Table 4. Comparison of estimation results of 1.4 mm and 1.8 mm fault sizes （n=2400 r/min）
模型 精确度/% RMSE 相对误差 SVR 86.42 0.3731 0.0741 DCNN 98.94 0.0772 0.1547 BiLSTM 49.77 0.3392 0.0339 DCNN+BiLSTM 99.94 0.0253 0.0125
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表 5 1.2 mm与2.0 mm损伤尺寸估计结果对比（n=2400 r/min）
Table 5. Comparison of estimation results of 1.2 mm and 2.0 mm fault sizes （n=2400 r/min）
模型 精确度/% RMSE 相对误差 SVR 79.77 0.1575 0.0765 DCNN 98.88 0.0605 0.0281 BiLSTM 69.12 0.2603 0.1324 DCNN+BiLSTM 99.48 0.0530 0.0252
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表 6 1.4 mm与1.8 mm损伤尺寸预测估计对比（n=1200 r/min）
Table 6. Comparison of estimation results of 1.4 mm and 1.8 mm fault sizes （n=1200 r/min）
模型 精确度/% RMSE 相对误差 SVR 87.37 0.1516 0.0871 DCNN 97.92 0.0645 0.0400 BiLSTM 92.08 0.1215 0.0637 DCNN+BiLSTM 99.44 0.0435 0.0266
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表 7 1.2 mm与2.0 mm损伤尺寸预测估计对比（n=1200 r/min）
Table 7. Comparison of estimation results of 1.2 mm and 2.0 mm fault sizes （n=1200 r/min）
模型 精确度/% RMSE 相对误差 SVR 88.87 0.1371 0.0728 DCNN 96.75 0.0752 0.0297 BiLSTM 83.25 0.1859 0.0918 DCNN+BiLSTM 99.77 0.0495 0.0246
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表 8 加噪声情况下1.4 mm与1.8 mm损伤尺寸估计结果对比（n=2400 r/min）
Table 8. Comparison of estimation results of 1.4 mm and 1.8 mm fault sizes by adding noise （n=2400 r/min）
模型 精确度/% RMSE 相对误差 SVR 50.15 0.1772 0.1058 DCNN 77.50 0.1640 0.0824 BiLSTM 50.41 0.2010 0.1265 DCNN+BiLSTM 98.67 0.0846 0.0414
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[1] LUO Maolin,GUO Yu,WU Xing,et al. An analytical model for estimating spalled zone size of rolling element bearing based on dual-impulse time separation[J]. Journal of Sound and Vibration,2019,453: 87-102. doi: 10.1016/j.jsv.2019.04.014 [2] KOGAN G,BORTMAN J,KLEIN R. A new model for spall-rolling-element interaction[J]. Nonlinear Dynamics,2017,87(1): 219-236. doi: 10.1007/s11071-016-3037-1 [3] XU L,CHATTERTON S,PENNACCHI P. Rolling element bearing diagnosis based on singular value decomposition and composite squared envelope spectrum[J]. Mechanical Systems and Signal Processing,2021,148: 1-25. [4] NIEHAUS W N,SCHMIDT S,HEYNS P S. NIC methodology: a probabilistic methodology for improved informative frequency band identification by utilizing the available healthy historical data under time-varying operating conditions[J]. Journal of Sound and Vibration,2020,488: 1-20. [5] WANG Youming,CHENG Lin. A combination of residual and long-short-term memory networks for bearing fault diagnosis based on time-series model analysis[J]. Measurement Science and Technology,2021,32(1): 1-14. [6] DOVEDI T,UPADHYAY R. Diagnosis of ball bearing faults using double decomposition technique[J]. International Journal of Acoustics and Vibration,2020,25(3): 327-340. doi: 10.20855/ijav.2020.25.31609 [7] TOMA R N,KIM J M. Bearing fault classification of induction motors using discrete wavelet transform and ensemble machine learning algorithms[J]. Applied Sciences-Basel,2020,10(15): 1-21. [8] REN Lei,SUN Yaqiang,CUI Jin,et al. Bearing remaining useful life prediction based on deep autoencoder and deep neural networks[J]. Journal of Manufacturing Systems,2018,48: 71-77. doi: 10.1016/j.jmsy.2018.04.008 [9] KHAN M A,KIM Y H,CHOO J. Intelligent fault detection using raw vibration signals via dilated convolutional neural networks[J]. Journal of Supercomputing,2020,76(10): 8086-8100. doi: 10.1007/s11227-018-2711-0 [10] ALJBALI S, ROY K. Anomaly detection using bidirectional LSTM[C]//Intelligent systems and applications. London: Springer, 2020: 612-619. [11] PLAKIA S,BOUTALIS Y S. Fault detection and identification of rolling element bearings with attentive dense CNN[J]. Neurocomputing,2020,405: 208-217. doi: 10.1016/j.neucom.2020.04.143 [12] SRIVASTAVA N,HINTON G,KRIZHEVSKY A,et al. Dropout: a simple way to prevent neural networks from overfitting[J]. Journal of Machine Learning Research,2014,15(1): 1929-1958. [13] CHEMALI E,KOLLMEYER P J,PREINDL M,et al. Long short-term memory-networks for accurate state of charge estimation of li-ion batteries[J]. IEEE Transactions on Industrial Electronics,2017,65(8): 6730-6739. [14] KONG Weicong,DONG Zhaoyang,JIA Youwei,et al. Short-term residential load forecasting based on LSTM recurrent neural network[J]. IEEE Transactions on Smart Grid,2017,10(1): 841-851. [15] EPPS I. An investigation into vibrations excited by discrete faults in rolling element bearings[D]. Christchurch, New Zealand: University of Canterbury, 1991. [16] RANDALL R B. Vibration-based condition monitoring: industrial, aerospace and automotive applications[M]. Australia: John Wiley&Sons, 2011. [17] SAWALHI N,RANDALL R B. Vibration response of spalled rolling element bearings: observations, simulations and signal processing techniques to track the spall size[J]. Mechanical Systems and Signal Processing,2011,25(3): 846-870. doi: 10.1016/j.ymssp.2010.09.009 [18] JASTRZEBSKA A. Lagged encoding for image-based time series classification using convolutional neural[J]. Statistical Analysis and Data Mining,2020,13(3): 245-260. doi: 10.1002/sam.11455 [19] 张向阳,陈果,郝腾飞,等. 基于机匣信号的滚动轴承故障卷积神经网络诊断方法[J]. 航空动力学报,2019,34(12): 2729-2737. doi: 10.13224/j.cnki.jasp.2019.12.022ZHANG Xiangyang,CHEN Guo,HAO Tengfei,et al. Convolutional neural network diagnosis method of rolling bearing fault based on casing signal[J]. Journal of Aerospace Power,2019,34(12): 2729-2737. (in Chinese) doi: 10.13224/j.cnki.jasp.2019.12.022 -
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