利用FCKT以及深度自编码神经网络的滚动轴承故障智能诊断

doi:10.3901/JME.2019.07.065

机械工程学报 ›› 2019, Vol. 55 ›› Issue (7): 65-72.doi: 10.3901/JME.2019.07.065

• 基于深度学习的机械装备故障预测与健康管理 • 上一篇下一篇

利用FCKT以及深度自编码神经网络的滚动轴承故障智能诊断

杨蕊, 李宏坤, 王朝阁, 郝佰田

大连理工大学机械工程学院大连 116024

收稿日期:2018-05-28 修回日期:2018-11-08 出版日期:2019-04-05 发布日期:2019-04-05
通讯作者: 李宏坤(通信作者),男,1974年出生,教授,博士研究生导师。主要研究方向为机械设备动态分析与故障诊断,信号处理。E-mail:lihk@dlut.edu.cn
作者简介:杨蕊,女,1990年出生,博士研究生。主要研究方向为机械设备微弱故障特征提取、智能故障诊断。E-mail:yr90223@mail.dlut.edu.cn
基金资助:
国家自然科学基金资助项目（51175057）。

Intelligent Fault Detection for Rolling Element Bearing Based on FCKT and Deep Auto-coding Neural Network

YANG Rui, LI Hongkun, WANG Chaoge, HAO Baitian

School of Mechanical Engineering, Dalian University of Technology, Dalian 116024

Received:2018-05-28 Revised:2018-11-08 Online:2019-04-05 Published:2019-04-05

摘要/Abstract

摘要： 实时、快速、批量地对振动信号进行处理成为故障诊断领域的未来发展趋势，但是可能会带来数据维数灾难问题。针对在样本较大情况下深度学习运行时间较长，以及层与层之间节点数的减少使得故障识别准确率降低问题，提出首先计算原始时域信号的频谱在不同偏移点数下的相关峭度值（FCKT）作为新的样本数据，并结合深度自编码神经网络实现轴承的智能故障分类。新样本相对于原始样本，实现了数据的维数约减，缩短了样本集的分析时间。同时，在保持各样本数据原有信息的基础上，使得样本之间差异性更突出。另外，该方法在避免深度学习算法层与层之间的权值根据经验设定的同时，解决了通过逐层减少隐含层节点数来提高计算效率时带来的分类识别准确率降低的问题。最后，通过试验数据对比分析验证了算法的有效性。

Abstract: Real-time, fast, and batch processing of vibration signals have become a future development trend in the field of fault diagnosis. However, it may lead to data dimensional disasters. In view of the fact that the long running time and the low fault identification accuracy of deep learning algorithm under the condition of large sample. The frequency spectrum correlation Kurtosis of original time domain signal is calculated under different iteration periods (FCKT) as new samples data and use the deep auto-coding neural network is used to realize the intelligent fault classification of planetary gearbox. Compared with the original samples, the new samples reduce the data dimension and shorten the analysis time. At the same time, the differences between the samples are more prominent based on the original information of each sample data. In addition, the method solves the problems that the weight parameters between layers of deep learning algorithm are set according to experience and the classification accuracy is reduced by reducing the number of hidden nodes layer by layer to improve the calculation efficiency. Finally, the validity of the algorithm is verified by comparison of experimental data.

Key words: correlation Kurtosis for different iteration periods (FCKT), deep auto-coding, intelligent fault classification

中图分类号:

TH113

杨蕊, 李宏坤, 王朝阁, 郝佰田. 利用FCKT以及深度自编码神经网络的滚动轴承故障智能诊断[J]. 机械工程学报, 2019, 55(7): 65-72.

YANG Rui, LI Hongkun, WANG Chaoge, HAO Baitian. Intelligent Fault Detection for Rolling Element Bearing Based on FCKT and Deep Auto-coding Neural Network[J]. Journal of Mechanical Engineering, 2019, 55(7): 65-72.

参考文献

[1] 雷亚国,贾峰,周昕,等. 基于深度学习理论的机械装备大数据健康监测方法[J]. 机械工程学报, 2015, 51(21):49-56. LEI Yaguo, JIA Feng, ZHOU Xin, et al. A deep learning-based method for machinery health monitoring with big data[J]. Journal of Mechanical Engineering,2015, 51(21):49-56.
[2] HINTON G E, SALAKHUTDINOV R R. Reducing the dimensionality of data with neural networks[J]. Science, 2006, 313(5786):504-507.
[3] TAO S, ZHANG T, YANG J, et al. Bearing fault diagnosis method based on stacked autoencoder and softmax regression[C]//Proceedings of the Control Conference, 2015:6331- 6335.
[4] JIA F, LEI Y, LIN J, et al. Deep neural networks:A promising tool for fault characteristic mining and intelligent diagnosis of rotating machinery with massive data[J]. Mechanical Systems & Signal Processing, 2016, 72:303-315.
[5] LEI Y, JIA F, LIN J, et al. An intelligent fault diagnosis method using unsupervised feature learning towards mechanical big data[J]. IEEE Transactions on Industrial Electronics, 2016, 63(5):3137-3147.
[6] SHAO H D, JIANG H K, LIN Y, et al. A novel method for intelligent fault diagnosis of rolling bearings using ensemble deep auto-encoders[J]. Mechanical Systems and Signal Processing, 2018, 102:278-297.
[7] SUN W, SHAO S, ZHAO R, et al. A sparse auto-encoder-based deep neural network approach for induction motor faults classification[J]. Measurement, 2016, 89:171-188.
[8] 陈仁祥,杨星,杨黎霞,等. 栈式稀疏加噪自编码深度神经网络的滚动轴承损伤程度诊断[J]. 振动与冲击, 2017, 36(21):125-131. CHEN Renxiang, YANG Xing, YANG Lixia, et al. Fault severity diagnosis method for rolling bearings based on a stacked sparse denoising auto-encoder[J]. Shock and Vibration, 2017, 36(21):125-131.
[9] CHEN R, CHEN S, HE M, et al. Rolling bearing fault severity identification using deep sparse auto-encoder network with noise added sample expansion[J]. Journal of Risk & Reliability, 2017, 231(6):666-679.
[10] AHMED H O A, WONG M L D, NANDI A K. Intelligent condition monitoring method for bearing faults from highly compressed measurements using sparse over-complete features[J]. Mechanical Systems & Signal Processing, 2018, 99:459-477.
[11] CHEN L, WANG Z Y, QIN W L, et al. Fault diagnosis of rotary machinery components using a stacked denoising autoencoder-based health state identification[J]. Signal Processing, 2017, 130:377-388.
[12] SCHOLKOPF B, SMOLA A, MULLER K R. Nonlinear component analysis as a kernel eigenvalue problem[J]. Neural Computation, 1998, 10(5):1299-1319.
[13] MCDONALD G L, ZHAO Q, ZUO M J. Maximum correlated Kurtosis deconvolution and application on gear tooth chip fault detection[J]. Mechanical Systems & Signal Processing, 2012, 33(1):237-255.
[14] CHEN X, ZHANG B, FENG F, et al. Optimal resonant band demodulation based on an improved correlated Kurtosis and its application in bearing fault diagnosis[J]. Sensors, 2017, 17(2):360-379.
[15] FENG J, LEI Y, SHAN H, et al. Early fault diagnosis of bearings using an improved spectral Kurtosis by maximum correlated Kurtosis deconvolution[J]. Sensors, 2015, 15(11):29363-29377.
[16] ZHOU H, CHEN J, DONG G. Application of maximum correlated Kurtosis deconvolution on rolling element bearing fault diagnosis[M]. London:Springer International Publishing, 2015.
[17] TANG G, WANG X, HE Y. Diagnosis of compound faults of rolling bearings through adaptive maximum correlated kurtosis deconvolution[J]. Journal of Mechanical Science and Technology, 2016, 30(1):43-54.

利用FCKT以及深度自编码神经网络的滚动轴承故障智能诊断

Intelligent Fault Detection for Rolling Element Bearing Based on FCKT and Deep Auto-coding Neural Network

PDF

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics

本文评价

[1]	陈永会, 张学良, 温淑花, 兰国生. 考虑弹塑性阶段的结合面法向接触阻尼分形模型[J]. 机械工程学报, 2019, 55(16): 58-68.
[2]	余磊, 刘莉, 马志赛, 康杰. 基于多维振动响应GSC-TARMA模型的时变结构模态参数辨识[J]. 机械工程学报, 2019, 55(15): 183-192.
[3]	谭博欢, 谢庆喜, 张农, 张邦基, 邓亢. 钢板弹簧迟滞特性建模及重载货车的动态响应分析[J]. 机械工程学报, 2019, 55(15): 193-201.
[4]	吴明, 马玉顺, 匡俊, 黄晓兵. 盘式永磁调速器在离心负载下的调速性能[J]. 机械工程学报, 2019, 55(14): 225-232.
[5]	黄家海, 贺亚彬, 于培, 赵斌. 落地式摩擦提升机建模和振动特性分析[J]. 机械工程学报, 2019, 55(12): 205-214.
[6]	严博, 马洪业, 韩瑞祥, 王珂, 武传宇. 可用于大幅值激励的永磁式非线性隔振器[J]. 机械工程学报, 2019, 55(11): 169-175.
[7]	王奉涛, 刘晓飞, 敦泊森, 邓刚, 韩清凯, 李宏坤. 基于萤火虫优化的核自动编码器在中介轴承故障诊断中的应用[J]. 机械工程学报, 2019, 55(7): 58-64.
[8]	陈旭, 朱才朝, 宋朝省, 谭建军, 朱永超. 紧急停机工况下风力发电机系统动态特性分析[J]. 机械工程学报, 2019, 55(5): 82-88.
[9]	张建华, 许晓林, 刘璇, 张明路. 双臂协调机器人相对动力学建模[J]. 机械工程学报, 2019, 55(3): 34-42.
[10]	谷勇霞, 张玉玲, 赵杰亮, 阎绍泽. 考虑含间隙关节的漂浮基空间机械臂动力学输出特性研究[J]. 机械工程学报, 2019, 55(3): 99-108.
[11]	刘杨, 石拓, 辛喜成, 马亚新, 薛曾元, 明帅帅. 转子挤压油膜阻尼器减振效率的理论研究[J]. 机械工程学报, 2019, 55(3): 90-98.
[12]	李庆, LIANG STEVEN Y. 非凸罚正则化稀疏低秩矩阵的大型减速机圆锥滚子轴承微弱故障诊断[J]. 机械工程学报, 2018, 54(23): 102-111.
[13]	马志赛, 丁千, 刘莉, 周思达. 线性时变结构模态参数时域辨识方法的研究进展[J]. 机械工程学报, 2018, 54(23): 137-159.
[14]	吴家腾, 杨宇, 程军圣. 基于解析有限元的齿根裂纹时变啮合刚度计算方法[J]. 机械工程学报, 2018, 54(23): 56-62.
[15]	廖永宜, 廖伯瑜. 基于模态柔度和能量分布的机床动态优化设计[J]. 机械工程学报, 2018, 54(23): 192-198.