自适应小波分析和多层卷积极限学习自编码器的轴承故障识别研究
Fault Identi fication of Rolling Bearing Based on Adaptive Wavelet Analysis and Multiple Layers Convolution Extreme Learning Auto-encoder
- 机车电传动 2021年0卷第6期 页码：106-113
- 作者机构：
1.重庆工程职业技术学院 智能制造与交通学院，重庆 402260
- 作者简介：
谭亚红（1983—），男，硕士，工程师，主要研究方向为电气控制与机器人；E-mail：tanyahong2020@163.com
- 基金信息：
DOI：10.13890/j.issn.1000-128x.2021.06.015
中图分类号：
扫 描 看 全 文
谭亚红. 自适应小波分析和多层卷积极限学习自编码器的轴承故障识别研究[J]. 机车电传动, 2021,0(6):106-113.
Yahong TAN. Fault Identi fication of Rolling Bearing Based on Adaptive Wavelet Analysis and Multiple Layers Convolution Extreme Learning Auto-encoder[J]. Electric Drive for Locomotives, 2021,0(6):106-113.
谭亚红. 自适应小波分析和多层卷积极限学习自编码器的轴承故障识别研究[J]. 机车电传动, 2021,0(6):106-113. DOI： 10.13890/j.issn.1000-128x.2021.06.015.
Yahong TAN. Fault Identi fication of Rolling Bearing Based on Adaptive Wavelet Analysis and Multiple Layers Convolution Extreme Learning Auto-encoder[J]. Electric Drive for Locomotives, 2021,0(6):106-113. DOI： 10.13890/j.issn.1000-128x.2021.06.015.
摘要
针对滚动轴承振动信号由于强时变和强噪声等特性导致其故障难以辨识的问题，提出一种基于自适应小波分析（AWA）和多层卷积极限学习自编码器（MLCELAE）的滚动轴承故障识别模型。首先，提出一种新的轴承振动信号频谱边界检测方法，对信号频谱进行自适应分割，进而将信号分解为若干本征模态分量；然后选择较能反映轴承故障特征的模态分量并重构；最后构造卷积极限学习自编码器，并逐层堆叠建立深层网络MLCELAE，将信号样本输入MLCELAE进行自动特征学习与故障识别。试验结果表明：提出方法的平均故障识别准确率达到了98.48%，标准差仅为0.17，相比于其他方法在轴承故障识别准确率方面更具优势，适用于滚动轴承故障的自动识别。
Abstract
Aiming at the problems of rolling bearing vibration signals were dif ficult to identify due to strong time-varying and strong noisy characteristics, a method based on adaptive wavelet analysis (AWA) and multiple layers convolution extreme learning auto-encoder (MLCELAE) was proposed. Firstly, a new method was proposed to detect the vibration signals spectrum boundary of rolling bearing, which could divide the signals spectrum adaptively, and then decompose the vibration signals into several intrinsic modal components. Secondly, the components which could best reflect the fault characteristics of the vibration signals were screened and reconstructed. Finally, the convolution extreme learning auto-encoder was constructed and multiple layers convolution extreme learning auto-encoder was built by stacking layer by layer, then the vibration signals samples of rolling bearing were fed into MLCELAE for automatic feature learning and fault identi fication. The experimental results show that the average fault identification accuracy of the proposed method reaches 98.48% and the standard deviation is only 0.17. Compared with other methods, it has more advantages in fault identi fication accuracy of rolling bearing, which is suitable for automatic identi fication of rolling bearing faults.
关键词
轴承自适应小波分析故障识别卷积极限学习自编码器
Keywords
bearingadaptive wavelet analysisfault identi ficationconvolution extreme learning auto-encoder
references
李长青, 林建辉, 胡永旭. 优化参数VMD和MED在列车齿轮箱滚动轴承故障诊断中的应用[J]. 机车电传动, 2020(3): 142-147.
LI Changqing, LIN Jianhui, HU Yongxu. Application of optimization parameters VMD and MED in fault diagnosis of train gearbox rolling bearings[J]. Electric Drive for Locomotives, 2020(3): 142-147.
刘玉婷, 林建辉. 改进的EWT方法在高速列车轴承故障诊断中的应用[J]. 机车电传动, 2020(4): 47-53.
LIU Yuting, LIN Jianhui. Application of improved EWT method in bearing fault diagnosis of high-speed train[J]. Electric Drive for Locomotives, 2020(4): 47-53.
王一宾, 李田力, 程玉胜, 等. 基于核极限学习机自编码器的标记分布学习[J]. 山东大学学报(工学版), 2020, 50(3): 58-65.
WANG Yibin, LI Tianli, CHENG Yusheng, et al. Label distribution learning based on kernel extreme learning machine auto-encoder[J]. Journal of Shandong University:Engineering Science, 2020, 50(3): 58-65.
崔鹏宇, 王泽勇, 邱春蓉, 等. 基于多尺度排列熵与双核极限学习机的滚动轴承故障诊断方法[J]. 电子测量与仪器学报, 2019, 33(5): 142-147.
CUI Pengyu, WANG Zheyong, QIU Chunrong, et al. Fault diagnosis of rolling bearings based on multi-scale permutation entropy and dual kernel extreme learning machine[J]. Journal of Electronic Measurement and Instrumentation, 2019, 33(5): 142-147.
MAO W T, FENG W S, LIU Y M, et al. A new deep auto-encoder method with fusing discriminant information for bearing fault diagnosis[J]. Mechanical Systems and Signal Processing, 2021, 150(12): 107233.
SHAO H D, JIANG H K, ZHAO K W, et al. A novel tracking deep wavelet auto-encoder method for intelligent fault diagnosis of electric locomotive bearings[J]. Mechanical Systems and Signal Processing, 2018, 110: 193-209.
张鑫, 郭顺生, 李益兵, 等. 基于拉普拉斯特征映射和深度置信网络的半监督故障识别[J]. 机械工程学报, 2020, 56(1): 69-81.
ZHANG Xin, GUO Shunsheng, LI Yibing, et al. Semi-supervised fault identi fication based on Laplacian eigenmap and deep belief networks[J]. Journal of Mechanical Engineering, 2020, 56(1): 69-81.
胡茑庆, 陈徽鹏, 程哲, 等. 基于经验模态分解和深度卷积神经网络的行星齿轮箱故障诊断方法[J]. 机械工程学报, 2019, 55(7): 9-18.
HU Niaoqing, CHEN Huipeng, CHENG Zhe, et al. Fault diagnosis for planetary gearbox based on EMD and deep convolutional neural networks[J]. Journal of Mechanical Engineering, 2019, 55(7): 9-18.
吴雅朋, 王吉芳, 徐小力, 等. 基于小波分析的FastICA联合降噪方法在滚动轴承故障诊断中的应用研究[J]. 中国机械工程, 2017, 28(18): 2183-2188.
WU Yapeng, WANG Jifang, XU Xiaoli, et al. Application research of FastICA noise reduction method based on wavelet analysis in fault diagnosis of rolling bearing[J]. China Mechanical Engineering, 2017, 28(18): 2183-2188.
李宇恒, 蒋章雷, 梁好, 等. 基于EEMD对称差分能量谱的行星齿轮箱故障诊断[J]. 组合机床与自动化加工技术, 2020(10): 122-125.
LI Yuheng, JIANG Zhanglei, LIANG Hao, et al. Fault diagnosis of planetary gearbox based on EEMD symmetrical energy spectrum[J]. Modular Machine Tool and Automatic Manufacturing Technique, 2020(10): 122-125.
池永为, 杨世锡, 焦卫东, 等. 基于EMD-DCS的滚动轴承伪故障特征识别方法[J]. 振动与冲击, 2020, 39(9): 9-16.
CHI Yongwei, YANG Shixi, JIAO Weidong, et al. EMD-DCS based pseudo-fault feature identification method for rolling bearings[J]. Journal of Vibration and Shock, 2020, 39(9): 9-16.
GILLES J. Empirical wavelet transform[J]. IEEE Transactions on Signal Processing, 2013, 61(16): 3999-4010.
焦瀚晖, 胡明辉, 王星, 等. 基于特征快速构造与卷积神经网络的机泵故障识别研究[J]. 机电工程, 2020, 37(9): 1063-1068.
JIAO Hanhui, HU Minghui, WANG Xing, et al. Fault recognition of pump failure based on feature fast construction and CNN[J]. Journal of Mechanical &Electrical Engineering, 2020, 37(9): 1063-1068.
张颖, 张超, 王天正, 等. 基于改进固有时间尺度分解和谱峭度的轴承故障诊断方法[J]. 太阳能学报, 2017, 38(3): 699-706.
ZHANG Ying, ZHANG Chao, WANG Tianzheng, et al. Fault diagnosis method for bearing based on improved intrinsic time-scale decomposition and spectrum kurtosis[J]. Acta Energiae Solaris Sinica, 2017, 38(3): 699-706.
JIANG H K, LI C L, LI H X. An improved EEMD with multiwavelet packet for rotating machinery multi-fault diagnosis[J]. Mechanical Systems and Signal Processing, 2013, 36(2): 225-239.
0
浏览量
26
下载量
0
CSCD
1
CNKI被引量
- 网络PDF下载
- Meta-XML下载
- BibTeX下载
- Endnote下载
- RefMan 下载
- NoteExpress下载
- NoteFirst下载
关联资源
相关文章
相关作者
相关机构
- 技术支持由北京北大方正电子有限公司提供 湘ICP备20010475号-1
京公网安备11010802024621 - 本系统建议在Chrome、 IE9+ 以上版本浏览器阅读本站内容，360浏览器请切换至极速模式
- Cookies帮助我们提供服务并提供个性化体验。使用本网站，即表示您同意我们使用Cookies