Citation: | LIANG Ruijun, RAN Wenfeng, YU Chuanliang, CHEN Weifang, NI De. Recognition of gearbox operation fault state based on CWT-CNN[J]. Journal of Aerospace Power, 2021, 36(12): 2465-2473. doi: 10.13224/j.cnki.jasp.20210450 |
[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]∥
|
[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.
|