基于SIR多级残差连接密集网络的轴承故障诊断

兰州理工大学学报 ›› 2022, Vol. 48 ›› Issue (6): 46-54.

基于SIR多级残差连接密集网络的轴承故障诊断

赵小强^*1,2,3, 罗维兰^1,2, 梁浩鹏^1,2

1.兰州理工大学电气工程与信息工程学院, 甘肃兰州 730050;
2.兰州理工大学甘肃省工业过程先进控制重点实验室, 甘肃兰州 730050;
3.兰州理工大学国家级电气与控制工程实验室教学中心, 甘肃兰州 730050

收稿日期:2021-05-18 出版日期:2022-12-28 发布日期:2023-03-21
通讯作者: 赵小强(1969-),男,陕西岐山人,博士,教授,博导.Email:xqzhao@lut.edu.cn
基金资助:
甘肃省科技计划资助(21YF5GA072,21JR7RA206),甘肃省教育厅产业支撑计划项目(2021CYZC-02)

Bearing fault diagnosis based on SIR multistage residual connection dense network

ZHAO Xiao-qiang^1,2,3, LUO Wei-lan^1,2, LIANG Hao-peng^1,2

1. College of Electrical Engineering and Information Engineering, Lanzhou Univ. of Tech., Lanzhou 730050, China;
2. Key Laboratory of Gansu Advanced Control for Industrial Processes, Lanzhou Univ. of Tech., Lanzhou 730050, China;
3. National Experimental Teaching Center of Electrical and Control Engineering, Lanzhou Univ. of Tech., Lanzhou 730050, China

Received:2021-05-18 Online:2022-12-28 Published:2023-03-21

摘要/Abstract

摘要： 为了研究旋转机械的滚动轴承在复杂工况下从时变性强、微弱信号中提取特征信息的性能,提出了基于SIR多级残差连接密集网络的轴承故障诊断方法.首先,设计SIR模块,该模块将对输入的数据特征通道赋予不同的权重并拓宽网络的宽度,提取更加重要、更加丰富的特征信息;其次,设计多级残差连接密集网络自适应提取轴承振动信号中的有效特征;最后,构建softmax分类器实现故障分类.通过与多种方法进行对比,实验结果表明,该方法在变噪声、变负荷和变工况下都能够更加准确地检测出故障,对复杂的工况环境更具有鲁棒性和泛化能力.

关键词: 故障诊断, 滚动轴承, 残差密集网络, 特征重标定, 变工况

Abstract: In order to study the performance of extracting feature information from time-varying and weak signals of rolling bearings in rotating machinery under complex working conditions, a bearing fault diagnosis method based on SIR multi-stage residual connection dense network is proposed. Firstly, the SIR module is designed, which extracts more important and richer feature information by giving different weights to the input data feature channels and broadening the width of the network. Secondly, a multi-stage residual connection dense network is designed to adaptively extract the effective features from the bearing vibration signals. Finally, a softmax classifier is constructed to realize fault classification. Compared with other methods, the experimental results show that the proposed method can detect faults more accurately under variable noise, variable load, and variable working conditions, and has more robustness and generalization ability for complex working conditions.

Key words: fault diagnosis, rolling bearing, residual density network, feature recalibration, variable conditions

中图分类号:

TH133.33

赵小强, 罗维兰, 梁浩鹏. 基于SIR多级残差连接密集网络的轴承故障诊断[J]. 兰州理工大学学报, 2022, 48(6): 46-54.

ZHAO Xiao-qiang, LUO Wei-lan, LIANG Hao-peng. Bearing fault diagnosis based on SIR multistage residual connection dense network[J]. Journal of Lanzhou University of Technology, 2022, 48(6): 46-54.

参考文献

[1] 鄢小安.基于数学形态学的滚动轴承故障诊断方法研究 [D].南京:东南大学,2019.
[2] WANG Xiang,ZHENG Yuan,ZHAO Zhenzhou,et al.Bearing fault diagnosis based on statistical locally linear embedding [J].Sensors,2015,15(7):16225-16247.
[3] CAESARENDRA W,WIDODO A,YANG B-S.Application of relevance vector machine and logistic regression for machine degradation assessment [J].Mechanical Systems and Signal Processing,2009,24(4):1161-1171.
[4] SAIDI L,FNAIECH F,HENAO H,et al.Diagnosis of broken-bars fault in induction machines using higher order spectral analysis [J].Isa Transactions,2013,52(1):140-148
[5] 胡爱军,赵军,孙尚飞,等.基于谱峭度和最大相关峭度解卷积的滚动轴承复合故障特征分离方法 [J].振动与冲击,2019,38(4):158-165.
[6] 程发斌,汤宝平,刘文艺.一种抑制维格纳分布交叉项的方法及在故障诊断中应用 [J].中国机械工程,2008(14):1727-1731.
[7] 祝小彦,王永杰,张钰淇,等.基于自适应最优Morlet小波的滚动轴承故障诊断 [J].振动.测试与诊断,2018,38(5):1021-1029,1085.
[8] PICHLER K,LUGHOFER E,PICHLER M,et al.Fault detection in reciprocating compressor valves under varying load conditions [J].Mechanical Systems and Signal Processing,2016,70/71:104-119.
[9] WANG Liming,SHAO Yimin.Fault feature extraction of rotating machinery using a reweighted complete ensemble empirical mode decomposition with adaptive noise and demodulation analysis [J].Mechanical Systems and Signal Processing,2020,138: 106545.
[10] JIANG Liying,LI Qianqian,CUI Jianguo,et al.Rolling bearing fault diagnosis based on Higher-order cumulants and BP neural network [C]//27th Chinese Control and Decision Conference.[S.l.]:IEEE,2015:2683-2686.
[11] 刘畅,伍星,刘韬,等.基于近似等距投影和支持向量机的滚动轴承故障诊断 [J].振动与冲击,2018,37(5):234-239.
[12] MURALIDHARAN V,SUGUMARAN V.A comparative study of Nave Bayes classifier and Bayes net classifier for fault diagnosis of monoblock centrifugal pump using wavelet analysis [J].Applied Soft Computing,2012,12(8):2023-2029.
[13] WANG Yalin,YANG Haibing, YUAN Xiaofeng ,et al.Deep learning for fault-relevant feature extraction and fault classification with stacked supervised auto-encoder [J].Journal of Process Control,2020,92:79-89.
[14] WANG Hongmei,LIU Pengzhong.Image recognition based on improved convolutional deep belief network model [J].Multimedia Tools and Applications,2020,80(9):1-15.
[15] 周奇才,沈鹤鸿,赵炯,等.基于改进堆叠式循环神经网络的轴承故障诊断 [J].同济大学学报(自然科学版),2019,47(10):1500-1507.
[16] 车畅畅,王华伟,倪晓梅,等.基于深度学习的航空发动机故障融合诊断 [J].北京航空航天大学学报,2018,44(3):621-628.
[17] 温江涛,闫常弘,孙洁娣,等.基于压缩采集与深度学习的轴承故障诊断方法 [J].仪器仪表学报,2018,39(1):171-179.
[18] 陶洁,刘义伦,付卓,等.基于Teager能量算子和深度置信网络的滚动轴承故障诊断 [J].中南大学学报(自然科学版),2017,48(1):61-68.
[19] 张弘斌,袁奇,赵柄锡,等.采用多通道样本和深度卷积神经网络的轴承故障诊断方法 [J].西安交通大学学报,2020,54(8):58-66.
[20] 张西宁,刘书语,余迪,等.改进深度卷积神经网络及其在变工况滚动轴承故障诊断中的应用 [J].西安交通大学学报,2021,55(6):1-8.
[21] 陈保家,陈学力,沈保明,等.CNN-LSTM深度神经网络在滚动轴承故障诊断中的应用 [J].西安交通大学学报,2021,55(6):28-36.
[22] HU J,SHEN L,ALBANIE S,et al.Squeeze-and-excitation networks [J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2020,42(8):2011-2023.
[23] SZEGEDY C,LOFFE S,VANHOUCKE V,et al.Inception-v4,inception-resnet and the impact of residual connections on learning [C]//Thirty-First Conference on Artificial Intelligence.San Francisco:[s.n.],2017:4278-4284.
[24] ZHANG Zhicheng,LIANG Xiaokun,DONG Xu ,et al.A sparse-view CT reconstruction method based on combination of densenet and deconvolution [J].IEEE Transactions on Medical Imaging, 2018,37(6):1407-1417.
[25] ZHANG Yulun,TIAN Yapeng,KONG Yu,et al.Residual dense network for image super-resolution [C]//2018 IEEE/CVF Conference on Computer Vision And Pattern Recognition.[S.l.]:IEEE,2018:2472-2481.
[26] 孙俊,何小飞,谭文军,等.空洞卷积结合全局池化的卷积神经网络识别作物幼苗与杂草 [J].农业工程学报,2018,34(11):159-165.
[27] SMITH W A and RANDALL R B.Rolling element bearing diagnostics using the Case Western Reserve University data:A benchmark study [J].Mechanical Systems and Signal Processing,2015,64/65:100-131.
[28] 赵小强,梁浩鹏.使用改进残差神经网络的滚动轴承变工况故障诊断方法 [J].西安交通大学学报,2020,54(9):23-31.