基于改进正余弦算法优化堆叠降噪自动编码器的电机轴承故障诊断

doi:10.19595/j.cnki.1000-6753.tces.210306

Abstract
Figure/Table
References
Related Citation (15)

Download: PDF (4768 KB) HTML (1 KB)
Export: BibTeX | EndNote (RIS)

Abstract The bearing is an important part of motor, but its fault vibration signal has noise interference, which makes feature extraction difficult. Stacked denoising auto encoders (SDAE) can effectively suppress the noise interference by setting the input data to zero and training the network randomly. In addition, the unsatisfactory combination of hyperparameters is likely to cause poor diagnostic performance of SDAE. Therefore, an improved sine cosine algorithm (ISCA) was proposed to optimize SDAE for motor bearing fault diagnosis. Firstly, the nonlinear inertia weight was introduced into the particle value update formula of sine cosine algorithm (SCA), and the control parameters were added with cosine change to construct ISCA. The hyperparameters of SDAE were adaptively selected by ISCA. Secondly, the unsupervised self-learning feature extraction method of SDAE model with optimal network structure was used to extract the characteristic parameters of vibration signals, so as to achieve better fault diagnosis effect. Simulation and field experiment results show that the proposed method has high convergence speed, high diagnosis accuracy and strong robustness, and has a good application prospect in motor bearing fault diagnosis.

Key words： Stacked denoising auto encoders improved sine cosine algorithm motor bearing fault diagnosis adaptive

Received: 11 March 2021

PACS:

TM307

	Service

	E-mail this article
	Add to my bookshelf
	Add to citation manager
	E-mail Alert
	RSS
	Articles by authors
	Li Bing
	Liang Shuqi
	Shan Wanning
	Zeng Wenbo
	He Yigang

Cite this article:

Li Bing,Liang Shuqi,Shan Wanning等. Motor Bearing Fault Diagnosis Based on Improved Sine and Cosine Algorithm for Stacked Denoising Autoencoders[J]. Transactions of China Electrotechnical Society, 2022, 37(16): 4084-4093.

URL:

https://dgjsxb.ces-transaction.com/EN/10.19595/j.cnki.1000-6753.tces.210306 OR https://dgjsxb.ces-transaction.com/EN/Y2022/V37/I16/4084

[1] 李兵, 韩睿, 何怡刚, 等. 改进随机森林算法在电机轴承故障诊断中的应用[J]. 中国电机工程学报, 2020, 40(4): 1310-1319, 1422.
Li Bing, Han Rui, He Yigang, et al.Applications of the improved random forest algorithm in fault diagnosis of motor bearings[J]. Proceedings of the CSEE, 2020, 40(4): 1310-1319, 1422.
[2] 王臻, 李承, 张舜钦, 等. 基于改进矩阵束滤波与检测的异步电动机故障诊断新方法[J]. 电工技术学报, 2015, 30(12): 213-219.
Wang Zhen, Li Cheng, Zhang Shunqin, et al.A new filter and detection approach of fault in asynchronous motor based on improved matrix pencil[J]. Transa- ctions of China Electrotechnical Society, 2015, 30(12): 213-219.
[3] 王芹芹, 刘瑞芳, 任雪娇. 基于多物理场分析的电机轴承放电击穿[J]. 电工技术学报, 2020, 35(20): 4251-4257.
Wang Qinqin, Liu Ruifang, Ren Xuejiao.The motor bearing discharge breakdown based on the multi- physics field analysis[J]. Transactions of China Electrotechnical Society, 2020, 35(20): 4251-4257.
[4] Chang Yong, Bao Guangqing, Cheng Sikai, et al.Improved VMD-KFCM algorithm for the fault diagnosis of rolling bearing vibration signals[J]. IET Signal Processing, 2021, 15(4): 238-250.
[5] 杨明, 董传洋, 徐殿国. 基于电机驱动系统的齿轮故障诊断方法综述[J]. 电工技术学报, 2016, 31(4): 58-63.
Yang Ming, Dong Chuanyang, Xu Dianguo.Review of gear fault diagnosis methods based on motor drive system[J]. Transactions of China Electrotechnical Society, 2016, 31(4): 58-63.
[6] 宋向金, 王卓, 胡静涛, 等. Hilbert解调制方法诊断异步电机轴承故障[J]. 电工技术学报, 2018, 33(21): 4941-4948.
Song Xiangjin, Wang Zhuo, Hu Jingtao, et al.Diagnosis of bearing fault in induction motors using Hilbert demodulation approach[J]. Transactions of China Electrotechnical Society, 2018, 33(21): 4941-4948.
[7] 程军圣, 王健, 桂林. 改进的EEMD方法及其在滚动轴承故障诊断中的应用[J]. 振动与冲击, 2018, 37(16): 51-56.
Cheng Junsheng, Wang Jian, Gui Lin.An improved EEMD method and its application in rolling bearing fault diagnosis[J]. Journal of Vibration and Shock, 2018, 37(16): 51-56.
[8] 陈鹏, 赵小强, 朱奇先. 基于多尺度排列熵和改进多分类相关向量机的滚动轴承故障诊断方法[J]. 电子测量与仪器学报, 2020, 34(2): 20-28.
Chen Peng, Zhao Xiaoqiang, Zhu Qixian.Rolling bearing fault diagnosis method based on multi-scale permutationentropy and improved multi-class relevance vector machine[J]. Journal of Electronic Measurement and Instrumentation, 2020, 34(2): 20-28.
[9] 周念成, 廖建权, 王强钢, 等. 深度学习在智能电网中的应用现状分析与展望[J]. 电力系统自动化, 2019, 43(4): 180-191.
Zhou Niancheng, Liao Jianquan, Wang Qianggang, et al.Analysis and prospect of deep learning application in smart grid[J]. Automation of Electric Power Systems, 2019, 43(4): 180-191.
[10] Yu Jianbo, Zhang Chengyi.Manifold regularized stacked autoencoders-based feature learning for fault detection in industrial processes[J]. Journal of Process Control, 2020, 92: 119-136.
[11] 陈俊生, 李剑, 陈伟根, 等. 采用滑动窗口及多重加噪比堆栈降噪自编码的风电机组状态异常检测方法[J]. 电工技术学报, 2020, 35(2): 346-358.
Chen Junsheng, Li Jian, Chen Weigen, et al.A method for detecting anomaly conditions of wind turbines using stacked denoising autoencoders with sliding window and multiple noise ratios[J]. Transa- ctions of China Electrotechnical Society, 2020, 35(2): 346-358.
[12] Zhao Xiaoping, Wu Jiaxin, Zhang Yonghong, et al.Fault diagnosis of motor in frequency domain signal by stacked de-noising auto-encoder[J]. Computers, Materials & Continua, 2018, 57(2): 223-242.
[13] 赵荣臻, 文云峰, 叶希, 等. 基于改进堆栈降噪自动编码器的预想事故频率指标评估方法研究[J]. 中国电机工程学报, 2019, 39(14): 4081-4092.
Zhao Rongzhen, Wen Yunfeng, Ye Xi, et al.Research on frequency indicators evaluation of disturbance events based on improved stacked denoising auto- encoders[J]. Proceedings of the CSEE, 2019, 39(14): 4081-4092.
[14] 侯文擎, 叶鸣, 李巍华. 基于改进堆叠降噪自编码的滚动轴承故障分类[J]. 机械工程学报, 2018, 54(7): 87-96.
Hou Wenqing, Ye Ming, Li Weihua.Rolling element bearing fault classification using improved stacked de-noising auto-encoders[J]. Journal of Mechanical Engineering, 2018, 54(7): 87-96.
[15] Mirjalili S.SCA: a sine cosine algorithm for solving optimization problems[J]. Knowledge-Based Systems, 2016, 96: 120-133.
[16] 徐松金, 龙文. 求解高维优化问题的改进正弦余弦算法[J]. 计算机应用研究, 2018, 35(9): 2574-2577.
Xu Songjin, Long Wen.Improved sine cosine algorithm for solving high-dimensional optimization problems[J]. Application Research of Computers, 2018, 35(9): 2574-2577.
[17] 闫炯程, 李常刚, 刘玉田. 基于SDAE特征提取的含风电电网可用输电能力计算[J]. 电力系统自动化, 2019, 43(1): 32-39.
Yan Jiongcheng, Li Changgang, Liu Yutian.Available transfer capability calculation in power system with wind power based on SDAE feature extraction[J]. Automation of Electric Power Systems, 2019, 43(1): 32-39.
[18] Vincent P, Larochelle H, Lajoie I, et al.Stacked denoising autoencoders: learning useful representation in a deep network with a local denoising criterion[J]. Journal of Machine Learning Research, 2010, 11(12): 3371-3408.
[19] Cheriyadat A M.Unsupervised feature learning for aerial scene classification[J]. IEEE Transactions on Geoscience and Remote Sensing, 2013, 52(1): 439-451.
[20] 李松柏, 康子剑, 陶洁. 基于信息融合及堆栈降噪自编码的齿轮故障诊断[J]. 振动与冲击, 2019, 38(5): 216-221.
Li Songbai, Kang Zijian, Tao Jie.Gear fault diagnosis based on information fusion and stacked de-noising auto-encoder[J]. Journal of Vibration and Shock, 2019, 38(5): 216-221.
[21] Ashfahani A, Pratama M, Lughofer E, et al.DEVDAN: deep evolving denoising autoencoder[J]. Neurocomputing, 2020, 390: 297-314.
[22] 郭文艳, 王远, 戴芳, 等. 基于精英混沌搜索策略的交替正余弦算法[J]. 控制与决策, 2019, 34(8): 1654-1662.
Guo Wenyan, Wang Yuan, Dai Fang, et al.Alternating sine cosine algorithm based on elite chaotic search strategy[J]. Control and Decision, 2019, 34(8): 1654-1662.
[23] Larochelle H, Bengio Y, Louradour J, et al.Exploring strategies for training deep neural networks[J]. Journal of Machine Learning Research, 2009, 10: 1-40.
[24] 王波, 刘树林, 张宏利. 基于QGA优化广义S变换的滚动轴承故障特征提取[J]. 振动与冲击, 2017, 36(5): 108-113, 119.
Wang Bo, Liu Shulin, Zhang Hongli.Fault feature extraction for rolling bearings based on generalized S transformation optimized with Quantum genetic algorithm[J]. Journal of Vibration and Shock, 2017, 36(5): 108-113, 119.
[25] 付东学, 赵希梅. 基于径向基函数神经网络的永磁直线同步电机反推终端滑模控制[J]. 电工技术学报, 2020, 35(12): 2545-2553.
Fu Dongxue, Zhao Ximei.Backstepping terminal sliding mode control based on radial basis function neural network for permanent magnet linear syn- chronous motor[J]. Transactions of China Electro- technical Society, 2020, 35(12): 2545-2553.
[26] 朱可恒. 滚动轴承振动信号特征提取及诊断方法研究[D]. 大连: 大连理工大学, 2013.