Fault diagnosis of rolling bearing based on CCNN-BiLSTMN method
JIN Jiangtao1, XU Zifei1, LI Chun1,2, MIAO Weipao1, SUN Kang1, XIAO Junqing1
1.College of Energy and Power Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China;
2.Shanghai Municipal Key Lab of Multiphase Flow and Heat Transfer in Power Engineering, Shanghai 200093, China
Abstract：The traditional rolling bearing fault diagnosis methods are difficult to diagnose in the environment of large noise and variable load. Based on chaos theory, CCNN-BiLSTM intelligent fault diagnosis method is proposed by using convolutional neural network (CNN) and bidirectional long short memory network (BiLSTM). The one-dimensional time series was transformed into two-dimensional chaotic series by the phase space reconstruction method, and the effective nonlinear information in the chaotic sequence was learned and extracted, which was input into the Softmax layer to complete the classification. The result show that, compared with the existing methods, the accuracy of the proposed CCNN-BiLSTM method is 3.76% and 5.21% higher under variable load and high noise (SNR=-8dB) environment respectively, which indicates that the proposed method has good robustness and generalization performance.
Key words: convolutional neural network; bi-directional long short term memory networks; chaos; bearing; fault diagnosis
金江涛1，许子非1，李春1,2，缪维跑1，孙康1，肖俊青1. 基于卷积双向长短期记忆网络与混沌理论的滚动轴承故障诊断[J]. 振动与冲击, 2022, 41(17): 160-169.
JIN Jiangtao1, XU Zifei1, LI Chun1,2, MIAO Weipao1, SUN Kang1, XIAO Junqing1. Fault diagnosis of rolling bearing based on CCNN-BiLSTMN method. JOURNAL OF VIBRATION AND SHOCK, 2022, 41(17): 160-169.
[1 ] Zhao B, Zhang X, Hai L, et al. Intelligent fault diagnosis of rolling bearings based on normalized CNN considering data imbalance and variable working conditions[J]. Knowledge-Based Systems, 2020, 199: 105971.
[ 2] Hu Z X, Wang Y, Ge M F, et al. Data-driven Fault Diagnosis Method based on Compressed Sensing and Improved Multi-scale Network[J]. IEEE Transactions on Industrial Electronics, 2020, 67(4): 3216-3225.
[3 ] 唐斯，陈新楚，郑松．基于注意力与多尺度卷积神经网络的电机轴承故障诊断[J]．电气技术，2020，21(11)：32-38．
Tang S, Chen X C, Zheng S. Fault diagnosis method of motor bearing based on attention and multi-scale convolution neural network[J]. Electrical Engineering, 2020, 21(11): 32-38.
[4 ] 许凡．基于振动信号特征提取的机械设备故障诊断方法研究[D]．武汉大学，2017．
[5 ] Liu R, Yang B, Zio E, et al. Artificial intelligence for fault diagnosis ofrotating machinery: A review[J]. Mechanical Systems & Signal Processing, 2018, 108: 33-47.
[6 ] 丁承君，冯玉伯，王曼娜．基于变分模态分解与深度卷积神经网络的滚动轴承故障诊断[J]．振动与冲击，2021，40(02)：287-296．
Ding C J, Feng Y B, Wang M N. Rolling bearing fault diagnosis using variational mode decomposition and deep convolutional neural network[J]. Journal of Vibration and Shock, 2021, 40(02): 287-296.
[7 ] 任浩，屈剑锋，柴毅，等．深度学习在故障诊断领域中的研究现状与挑战[J]．控制与决策，2017，32(8)：1345-1358．
Ren H, Qu J F, Chai Y, et al. Deep learning for fault diagnosis: the state of the art and challenge[J]. Control and Decision, 2017, 32(8): 1345-1358.
[8 ] Lee K B, Cheon S, Chang O K. A convolutional neural network for fault classification and diagnosis in semiconductor manufacturing processes[J]. IEEE Transactions on Semiconductor Manufacturing, 2017, 30(2): 135-142.
[9 ] Janssens O, Slavkovikj V, Vervisch B, et al. Convolutional neural network based fault detection for rotating machinery[J]. Journal of Sound ＆ Vibration, 2016, 377: 331-345.
[10 ] 贾京龙，余涛，吴子杰，等．基于卷积神经网络的变压器故障诊断方法[J]．电测与仪表，2017，54(13)：62-67．
Jia J L, Yu T, Wu Z J, et al. Fault diagnosis method of transformer based on convolutional neural network[J]. Electrical Measurement & Instrumentation, 2017, 54(13): 62-67.
[11 ] Chen Z Q, Li C, Sanchez R V. Gearbox Fault Identification and Classification with Convolutional Neural Networks[J]. Shock and Vibration, 2015, 2015(PT.5):1-10.
[12 ] Zhao B, Zhang X, Hai L, et al. Intelligent fault diagnosis of rolling bearings based on normalized CNN considering data imbalance and variable working conditions[J]. Knowledge-Based Systems, 2020, 199: 105971.
[13 ] 李思琦，蒋志坚．基于EEMD-CNN的滚动轴承故障诊断方法[J]．机械强度，2020，42(05)：1033-1038．
Li S Q, Jiang Z J. Fault diagnosis method of rolling bearing based on EEMD-CNN[J]. Journal of Mechanical Strength, 2020, 42(05): 1033-1038.
[14 ] SMALE S. Differentiable dynamical systems. Uspekhi Mat. Nauk, 1970, 25(1): 113-185.
[15 ] 和志强，杨建，罗长玲．基于BiLSTM神经网络的特征融合短文本分类算法[J]．智能计算机与应用，2019，9(02)：21-27．
He Z Q, Yang J, Luo C L. Combination characteristics based on BiLSTM for short text classification[J]. Intelligent Computer and Applications, 2019, 9(02): 21-27.
[16 ] Schuster M, Paliwal K K. Bidirectional recurrent neural networks[J]. IEEE Transactions on Signal Processing, 1997, 45(11): 2673-2681.
[17 ] 赵志宏，赵敬娇，魏子洋．基于BiLSTM的滚动轴承故障诊断研究[J]．振动与冲击，2021，40(01)：95-101．
Zhao Z H, Zhao J J, Wei Z Y. Rolling bearing fault diagnosis based on BiLSTM network[J]. Journal of Vibration and Shock, 2021, 40(01): 95-101.
[18 ] Sterman J D. Deterministic chaos in models of human behavior: Methodological issues and experimental results[J]. System Dynamics Review, 1988, 4(1): 148-178.
[19 ] 宋锐．基于混沌激励的结构损伤识别研究[D]．东南大学，2015．
Song R. Structural damage identification based on chaos incentive[D]. Southeast University, 2015.
[20 ] Abarbanel H D I, Brown R, Sidorowich J J, et al. The analysis of observed chaotic data in physical systems[J]. Reviews of modern physics, 1993, 65(4): 1331-1392.
[21 ] 张菁，樊养余，李慧敏，等．相空间重构中延迟时间选取的新算法[J]．计算物理，2011，28(3)：469-474．
Zhang J, Fan Y Y, Li H M, et al. An improved algorithm for choosing delay time in phase space reconstruction[J]. Chinese Journal of Computational Physics, 2011, 28(3): 469-474.
[22 ] 张文超，谭思超，高璞珍．基于Lyapunov指数的摇摆条件下自然循环流动不稳定性混沌预测[J]．物理学报，2013，62(6)：53-60．
Zhang W C, Tan S C, Gao P Z. Chaotic forecasting of natural circulation flow instabilities under rolling motion based on Lyapunov exponents[J]. Acta Physica Sinica, 2013, 62(6): 53-60.
[23 ] 许子非，缪维跑，李春，等．流场非线性特征提取与混沌分析[J]．物理学报，2020，69(24)：344-352．
Xu Z F, Miao W P, Li C, et al. Nonlinear feature extraction
and chaos analysis of flow field[J]. Acta Physica Sinica, 2020, 69(24): 344-352.
[24 ] Wolf A, Swift J B, Swinney H L, et al. Determining Lyapunov exponents from time series[J]. Physica D, 1985, 16(2): 285-371.
[25 ] 李彦冬，郝宗波，雷航．卷积神经网络研究综述[J]．计算机应用，2016，36(9)：2508-2515．
Li Y D, Hao Z B, Lei H. Survey of convolutional neural network[J]. Journal of Computer Applications, 2016, 36(9): 2508-2515.
[26 ] Krizhevsky A, Sutskever I, Hinton G. ImageNet Classification with Deep Convolutional Neural Networks[C]// NIPS. Curran Associates Inc. 2012.
[27 ] Case Western Reserve University, Bearing Data Center (Seeded Fault Test Data)[EB/OL]. (2013-08-16) [2017-01-02]. http://csegroups.case. cedu /bearing data center/home.
