针对传统滚动轴承故障诊断模型对工程先验知识依赖性强、提取特征不充分、分类器选取困难等问题,提出一种基于多尺度深度卷积网络特征融合的滚动轴承故障诊断模型。首先,建立集特征提取与模式识别于一体的卷积神经网络模型,利用小波变换将滚动轴承振动信号转换为二维图像作为输入样本集。然后,在网络结构中构建多尺度特征融合模块自适应提取故障样本不同层级特征,以实现样本不同尺度特征的充分提取。最后,将故障样本输入到网络中实现轴承信号特征自适应提取及端到端诊断。实验结果表明,所提基于多尺度深度卷积网络特征融合的故障诊断模型能充分提取信号各层级特征,在不同噪声干扰下具有较高的诊断精度和鲁棒性,可为滚动轴承故障诊断提供理论基础和实现途径。
Abstract
Aiming for the limitations of traditional fault diagnosis model, such as, strong dependence on engineering priori knowledge, incomplete feature extraction, difficulties in selection of classifiers, a fault diagnosis model of rolling bearing based on feature fusion of multi-scale deep convolutional neutral network is proposed. First, a convolutional neural network model that integrates feature extraction and pattern recognition is constructed, the vibration signals of rolling bearing are converted into two-dimensional images by wavelet transform and used as the input sample set. Second, a multi-scale feature fusion module is built up in the network structure for the purpose of adaptive extraction of features at different levels of fault samples, aiming to extract different-scale features completely. Finally, fault samples are input into the network to realize adaptive features extract of bearing signals and end-to-end diagnosis. According to the experimental analysis results, the proposed fault diagnosis model based on feature fusion of multi-scale deep convolutional network can extract features of the signal at all levels and achieves higher diagnosis accuracy and robustness under interferences of different noises. It provides a theoretical basis to realize fault diagnosis of rolling bearings.
关键词
风电机组 /
滚动轴承 /
故障诊断 /
小波变换 /
多尺度卷积神经网络 /
特征融合
Key words
wind turbines /
rolling bearings /
fault diagnosis /
wavelet transforms /
multi-scale convolutional neural network /
feature fusion
{{custom_sec.title}}
{{custom_sec.title}}
{{custom_sec.content}}
参考文献
[1] 陈雪峰, 郭艳婕, 许才彬, 等. 风电装备故障诊断与健康监测研究综述[J]. 中国机械工程, 2020, 31(2): 175-189.
CHEN X F, GUO Y J, XU C B, et al. Summary of research on fault diagnosis and health monitoring of wind power equipment[J]. China mechanical engineering, 2020, 31(2): 175-189.
[2] 张东, 冯志鹏.基于变分模式分解和微积分增强能量算子的滚动轴承故障诊断[J]. 工程科学学报, 2016, 38(9): 1327-1334.
ZHANG D, FENG Z P.Fault diagnosis of rolling bearing based on variational mode decomposition and calculus enhanced energy operator[J]. Journal of engineering sciences, 2016, 38(9): 1327-1334.
[3] 谷晓娇, 陈长征.基于VMD和QPSO-SR的风电机组轴承故障提取方法[J]. 太阳能学报, 2019, 40(10): 2946-2952.
GU X J, CHEN C Z.Bearing fault extraction method of wind turbine based on VMD and QPSO-SR[J]. Acta energiae solaris sinica, 2019, 40(10): 2946-2952.
[4] 陈宗祥, 陈明星, 焦民胜, 等. 基于改进EMD和双谱分析的电机轴承故障诊断实现[J]. 电机与控制学报, 2018, 22(5): 78-83.
CHEN Z X, CHEN M X, JIAO M S, et al. Realization of motor bearing fault diagnosis based on improved EMD and bispectrum analysis[J]. Electric machines and control, 2018, 22(5): 78-83.
[5] 徐可, 陈宗海, 张陈斌, 等. 基于经验模态分解和支持向量机的滚动轴承故障诊断[J]. 控制理论与应用, 2019, 36(6): 915-922.
XU K, CHEN Z H, ZHANG C B, et al. Fault diagnosis of rolling bearing based on empirical mode decomposition and support vector machine[J]. Control theory and applications, 2019, 36(6): 915-922.
[6] 唐贵基, 田甜, 庞彬.基于快速谱相关和PSO-SVM的变工况滚动轴承状态识别[J]. 电力自动化设备, 2019, 39(7): 168-174.
TANG G J, TIAN T, PANG B.State recognition of rolling bearings under variable conditions based on fast spectral correlation and PSO-SVM[J]. Electric power automation equipment, 2019, 39(7): 168-174.
[7] 赵洪山, 刘辉海.基于深度学习网络的风电机组主轴承故障检测[J]. 太阳能学报, 2018, 39(3): 588-595.
ZHAO H S, LIU H H.Fault detection of wind turbine main bearing based on deep learning network[J]. Acta energiae solaris sinica, 2018, 39(3): 588-595.
[8] XU F, FANG Y J, WANG D, et al. Combining DBN and FCM for fault diagnosis of roller element bearings without using data labels[J]. Shock and vibration, 2018, 2018: 1-12.
[9] 陈志刚, 杜小磊, 王衍学, 等. 改进集成深层自编码器在轴承故障诊断中的应用[J]. 控制与决策, 2021, 36(1): 135-142.
CHEN Z G, DU X L, WANG Y X, et al. Application of improved integrated deep self-encoder in bearing fault diagnosis[J]. Control and decision, 2021, 36(1): 135-142.
[10] LIU H, ZHOU J Z, ZHENG Y, et al. Fault diagnosis of rolling bearings with recurrent neral network-based autoencoders[J]. ISA transactions, 2018, 77: 167-178.
[11] 汤宝平, 熊学嫣, 赵明航, 等. 多共振分量融合CNN的行星齿轮箱故障诊断[J]. 振动、测试与诊断, 2020, 40(3): 507-512, 625.
TANG B P, XIONG X Y, ZHAO M H, et al. Multi-resonance component fusion CNN-based planetary gearbox fault diagnosis[J]. Journal of vibration, measurement & diagnosis, 2020, 40(3): 507-512, 625.
[12] ABDELRAOUF Y K, NOUREDDINE G, MOHAMED N S, et al. Rolling element bearing fault diagnosis for rotating machinery using vibration spectrum imaging and convolutional neural networks[J]. The international journal of advanced manufacturing technology, 2020, 106(5): 1737-1751.
[13] 王奉涛, 薛宇航, 王洪涛, 等. GLT-CNN方法及其在航空发动机中介轴承故障诊断中的应用[J]. 振动工程学报, 2019, 32(6): 1077-1083.
WANG F T, XUE Y H, WANG H T, et al. GLT-CNN method and its application in fault diagnosis of aeroengine intermediate bearing[J]. Journal of vibration engineering, 2019, 32(6): 1077-1083.
[14] MA Y F, JIA X S, BAI H J, et al. A new fault diagnosis method based on convolutional neural network and compressive sensing[J]. Journal of mechanical science and technology, 2019, 33(11): 5177-5188.
[15] 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(5): 45-52.
ZHU H J, WANG X Q, RUI T, et al. Research on mechanical fault diagnosis based on translation invariant CNN[J]. Vibration and shock, 2019, 38(5): 45-52.
[16] 卢鹏, 邹佩岐, 邹国良.基于多尺度卷积特征融合的台风等级分类模型[J]. 激光与光电子学进展, 2019, 56(16): 9-15.
LU P, ZOU P Q, ZOU G L.Typhoon class classification model based on multi-scale convolution feature fusion[J]. Progress in laser and optoelectronics, 2019, 56(16): 9-15.
[17] 徐岩, 孙美双.基于多特征融合的卷积神经网络图像去雾算法[J]. 激光与光电子学进展, 2018, 55(3): 260-269.
XU Y, SUN M S.Image dehazing algorithm based on multi-feature fusion convolutional neural network[J]. Progress in laser and optoelectronics, 2018, 55(3): 260-269.
基金
国家自然科学基金（52065064; 51967019）; 中国博士后基金面上项目（2020M6773547）; 自治区研究生科研创新项目（XJ2021G056）
PDF(6315 KB)
