Recognition of gearbox operation fault state based on CWT-CNN
-
摘要: 针对传统故障诊断中提取的特征不具有自适应能力、很难匹配特定故障的问题,提出了一种基于连续小波变换(CWT)和二维卷积神经网络(CNN)的齿轮箱故障诊断方法。该方法对齿轮箱故障振动信号采用连续小波变换构造其时频图,以其为输入构建卷积神经网络模型,通过多层卷积池化形成深层分布式故障特征表达。利用反向传播算法调整网络各层的结构参数,使模型建立从信号特征到故障状态之间的准确映射。在不同工况和不同故障状态下的实验中,故障识别准确率达到了99.2%,验证了方法有效性。采用这种自适应学习信号中丰富的信息的方法,可以为故障诊断智能化提供基础。
-
关键词:
- 齿轮箱 /
- 连续小波变换(CWT) /
- 时频图 /
- 故障诊断 /
- 卷积神经网络(CNN)
Abstract: In view of the problem that the features extracted by the traditional fault diagnosis do not have adaptive ability and are difficult to match specific faults,a method for fault detection of gearbox based on the continuous wavelet transform (CWT) and two-dimensional convolutional neural network (CNN) was proposed.This method constructed the time-frequency diagrams to raw vibration signals through CWT,then built the CNN model using the diagrams as input,and finally formed a deep distributed fault feature expression through the multiple convolutions and pooling operations.The back propagation algorithm was used to adjust the structural parameters of each layer of the network,making the model establish an accurate mapping from the signal characteristics to the fault states.In experiments under different working conditions and fault states,the fault recognition accuracy reached 99.2%,which verified the effectiveness of the proposed method.Using this method of adaptive learning the abundant information in the signal can provide a basis for the intelligent fault diagnosis. -
[1] 代士超,郭瑜,伍星.基于同步平均与倒频谱编辑的齿轮箱滚动轴承故障特征量提取[J].振动与冲击,2015,34(21):205-209. [2] 付胜,徐斌,杜晓帆,等.基于奇异值分解和支持向量机的齿轮故障诊断[J].机械传动,2013,37(9):90-92,102. [3] JAWADEKAR A,PARASKAR S,JADHAV S,et al.Artificial neural network-based induction motor fault classifier using continuous wavelet transform[J].Systems Science and Control Engineering,2014,2(1):684-690. [4] LEE W,PARK C G.Double fault detection of cone-shaped redundant IMUs using wavelet transformation and EPSA[J].Sensors,2014,14(2):3428-3444. [5] LU Chen,WANG Zhenya,QIN Weili,et al.Fault diagnosis of rotary machinery components using a stacked denoising autoencoder-based health state identification[J].Signal Processing,2017,130:377-388. [6] 庞梦洋,索中英,郑万泽,等.基于RS-CART决策树的航空发动机小样本故障诊断[J].航空动力学报,2020,35(7):1559-1568. [7] TAMILSELVAN P,WANG Pingfeng.Failure diagnosis using deep belief learning based health state classification[J].Reliability Engineering and Systems Safety,2013,115(7):124-135. [8] 叶壮,余建波.基于多通道一维卷积神经网络特征学习的齿轮箱故障诊断方法[J].振动与冲击,2020,39(20):55-66. [9] 葛江华,刘奇,王亚萍,等.支持张量机与KNN-AMDM决策融合的齿轮箱故障诊断方法[J].振动工程学报,2018,31(6):1093-1101. [10] SAMANTA S,BERA J N,SARKAR G.KNN based fault diagnosis system for induction motor[C]∥ The 2nd International Conference on Control,Instrumentation,Energy and Communication (CIEC) .Kolkata,India:IEEE,2016,304-308.[11] SARAVANAN N,SIDDABATTUNI V N S K,RAMACHANDRAN K I.Fault diagnosis of spur bevel gear box using artificial neural network (ANN),and proximal support vector machine (PSVM)[J].Applied Soft Computing,2010,10(1):344-360. [12] 沈长青,朱忠奎,黄伟国,等.基于支持向量回归方法的齿轮箱故障诊断研究[J].振动、测试与诊断,2013,33(5):775-781,909. [13] ZHU Xingtong,XIONG Jianbin,LIANG Qing.Fault diagnosis of rotation machinery based on support vector machine optimized by quantum genetic algorithm[J].IEEE Access,2018,6:33583-33588. [14] 左红艳,刘晓波,洪连环.双阶自适应小波聚类的航空发动机故障分类与识别[J].振动工程学报,2018,31(1):165-175. [15] HAN Te,LIU Chao,YANG Wenguang,et al.A novel adversarial learning framework in deep convolutional neural network for intelligent diagnosis of mechanical faults[J].Knowledge-Based Systems,2019,165:474-487. [16] JIAO Jinyang,ZHAO Ming,LIN Jing,et al.Deep coupled dense convolutional network with complementary data for intelligent fault diagnosis[J].IEEE Transactions on Industrial Electronics,2019,66(12):9858-9867. [17] 杨平,苏燕辰.基于卷积门控循环网络的滚动轴承故障诊断[J].航空动力学报,2019,34(11):2432-2439. [18] LI Yibing,ZOU Li,JIANG Li,et al.Fault diagnosis of rotating machinery based on combination of deep belief network and one-dimensional convolutional neural network[J].IEEE Access,2019,7:165710-165723. [19] 张立智,徐卫晓,井陆阳,等.基于EMD-SVD和CNN的旋转机械故障诊断[J].振动、测试与诊断,2020,40(6):1063-1070,1228. [20] 圣文顺,孙艳文.卷积神经网络在图像识别中的应用[J].软件工程,2019,22(2):13-16.
点击查看大图
计量
- 文章访问数: 214
- HTML浏览量: 8
- PDF量: 243
- 被引次数: 0