基于Alexnet-Adaboost的多工况滚动轴承故障识别方法

摘要
图/表
参考文献(18)
相关文章 (15)

全文: PDF (1476 KB) HTML (1 KB)
输出: BibTeX | EndNote (RIS)

摘要针对实际工程中滚动轴承多工况下传统故障诊断方法识别率偏低的情况。论文提出了一种基于Alexnet-Adaboost相结合的滚动轴承故障识别方法。以滚动轴承信号的时频图作为模型输入，分类结果为模型输出，训练多个Alexnet基分类器；在此基础上利用Adaboost（自适应提升）算法进一步提升得到强分类器，将多工况下滚动轴承信号的时频图输入强分类器进行测试。结果显示，所提方法可实现对多工况下滚动轴承故障有效识别，并且在一定程度上提高了故障分类的准确性。

	服务

	把本文推荐给朋友
	加入我的书架
	加入引用管理器
	E-mail Alert
	RSS
	作者相关文章
	唐贵基
	田寅初
	田甜

关键词 ：状态识别, 时频图, 滚动轴承, 卷积神经网络, 自适应提升算法

Abstract：In view of the fact that the recognition rate of the traditional fault diagnosis method of rolling bearing is low under multiple working conditions in practical engineering. In this paper, a rolling bearing fault identification method based on Alexnet-Adaboost was proposed. Taking the time-frequency diagram of the rolling bearing signal as the model input and the classification result as the model output, several Alexnet-based classifiers were trained; on this basis, the Adaboost (adaptive lifting) algorithm was used to further improve the strong classifier, and the time-frequency diagram of the rolling bearing signal under multiple working conditions was input into the strong classifier for testing. The results show that the proposed method can effectively identify rolling bearing faults under multiple working conditions, and improves the fault classification accuracy to a certain extent.

Key words： Fault diagnosis time-frequency diagram rolling bearing convolutional neural network Adaboost

收稿日期: 2020-07-21 出版日期: 2022-01-28

引用本文:

唐贵基，田寅初，田甜. 基于Alexnet-Adaboost的多工况滚动轴承故障识别方法[J]. 振动与冲击, 2022, 41(2): 20-25.
TANG Guiji，TIAN Yinchu，TIAN Tian. Multi-working condition rolling bearing fault identification method based on the AlexNet-Adaboost algorithm. JOURNAL OF VIBRATION AND SHOCK, 2022, 41(2): 20-25.

链接本文:

http://jvs.sjtu.edu.cn/CN/ 或 http://jvs.sjtu.edu.cn/CN/Y2022/V41/I2/20

[1] LeCun Y, Bengio Y, Hinton G. Deep learning[J]. Nature. 2015, 521(7553): 436-444.
[2] 曲建岭,余路,袁涛,等.基于一维卷积神经网络的滚动轴承自适应故障诊断算[J].仪器仪表学报,2018,39(07):134-143.
QU Jianling, YU Lu, YUAN Tao, et al. Adaptive fault diagnosis algorithm for rolling bears based on one-dimensional convolutional neural network[J]. Chinese Journal of Scientific Instrument, 2018, 39(07): 134-143.
[3] Hoang DuyTang, Kang HeeJun. Rolling element bearing fault diagnosis using convolutional neural network and vibration image[J]. Cognitive Systems Research, 2018, 53(JAN.): 42-50.
[4] CHEN Z Q, LI C, SAN CHEZ R V. Gearbox fault identification and classification with convolutional neural networks[J]. Shock and Vibration, 2015(2):1-10.
[5] 朱会杰,王新晴,芮挺,等.基于平移不变CNN的机械故障诊断研究[J].振动与冲击,2019,38(05):45-52.
ZHU Huijie, WANG Xinqing, RUI Ting, et al. Machinery fault diagnosis based on shift invariant CNN[J]. Journal of Vibration and Shock, 2019, 38(05): 45-52.
[6] 张文风,周俊.基于Dropout-CNN的滚动轴承故障诊断研究[J].轻工机械,2019,37(02):62-67.
ZHANG Wenfeng, ZHOU Jun. Fault diagnosis method of rolling bearing based on Dropout-CNN[J]. 2019, 37(02): 62-67.
[7] 李恒,张氢,秦仙蓉,等.基于短时傅里叶变换和卷积神经网络的轴承故障诊断方法[J].振动与冲击,2018,37(19):124-131.
LI Heng, ZHANG Qing, QIN Xianrong, et al. Fault diagnosis method for rolling bearings based on short-time Fourier transform and convolution neural network[J]. Journal of Vibration and Shock, 2018, 37(19): 124-131.
[8] 万齐杨,熊邦书,李新民,等.基于DCAE-CNN的自动倾斜器滚动轴承故障诊断[J].振动与冲击,2020,39(11):273-279.
WAN Qiyang, XIONG Bangshu, LI Xinmin, et al. Fault diagnosis for rolling bearing of swashplate based on DCAE-CNN[J]. Journal of Vibration and Shock, 2020, 39(11): 273-279.
[9] 宫文峰,陈辉,张泽辉,等.基于改进卷积神经网络的滚动轴承智能故障诊断研究[J].振动工程学报,2020,33(02):400-413.
GONG Wenfeng, CHEN Hui, ZHANG Zehui, et al. Research on Intelligent Fault Diagnosis of Rolling Bearing Based on Improved Convolutional Neural Network[J]. Journal of Vibration Engineering, 2020, 33(02): 400-413.
[10] 赵小强,张青青,陈鹏,等.基于PSO-BFA和改进Alexnet的滚动轴承故障诊断方法[J].振动与冲击,2020,39(07):21-28.
ZHAO Xiaoqiang, ZHANG Qingqing, CHEN Peng, et al. Rolling bearing fault diagnosis method based on improved Alexnet and PSO-BFA[J]. Journal of Vibration and Shock, 2020, 39(07): 21-28.
[11] 蔡苗苗. 基于小波变换和神经网络的滚动轴承故障诊断系统[D].东北石油大学,2015.
CAI Miaomiao. Rolling bearing fault diagnosis system based on wavelet transform and neural network[D]. Northeast Petroleum University, 2015.
[12] Krizhevsky A, Sutskever I, Hinton G. Imagenet classification with deep convolutional neural networks[J]. Advances in neural information processing systems, 2012, 25(2).
[13] Freund Y, Schapire R E. A desicion-theoretic generalization of on-line learning and an application to boosting[J]. Journal of Computer and System ences, 1995, 55: 119-139.
[14] Zhu J, Rosset S, Zou H, et al. Multi-class adaboost[J]. Statistics and its interface, 2006, 2(3).
[15] 蒙志强,董绍江,潘雪娇,等.基于改进卷积神经网络的滚动轴承故障诊断[J].组合机床与自动化加工技术,2020(02):79-83.
MENG Zhiqiang, DONG Shaojiang, PAN Xuejiao, et al. Fault diagnosis of rolling bearing based on improved convolutional neural network[J]. Modular Machine Tool & Automatic Manufacturing Technique, 2020(02): 79-83.
[16] 赵小强,张青青.改进Alexnet的滚动轴承变工况故障诊断方法[J].振动.测试与诊断,2020,40(03):472-480+623.
ZHAO Xiaoqiang， ZAHNG Qingqing. Improved Alexnet Based Fault Diagnosis Method for Rolling Bearing Under Variable Conditions[J]. Journal of Vibration,Measurement & Diagnosis,2020,40(03):472-480+623.
[17] Ruder S . An overview of gradient descent optimization algorithms[J]. 2016.
[18] Case Western Reserve University Bearing Data Center[EB/OL]. http://cse groups.case.edu/bearing data center/ pages/download-data-file.