Fault diagnosis method of rolling bearings based on SSD and 1DCNN

被引：0

作者：

Song L. ^{[1
,2
]}

Su L. ^{[1
,2
]}

Li K. ^{[1
,2
]}

Su W. ^{[3
]}

机构：

[1] School of Mechanical Engineering, Jiangnan University, Wuxi

[2] Jiangsu Key Laboratory of Advanced Food Manufacturing Equipment & Technology, Jiangnan University, Wuxi

[3] Jiangsu Province Special Equipment Safety Supervision Inspection Institute Branch of Wuxi, Wuxi

来源：

| 1600年 / Huazhong University of Science and Technology卷 / 48期

关键词：

1D convolutional neural network (1DCNN); Fault diagnosis; Kurtosis; Rolling bearing; Singular spectrum decomposition (SSD);

D O I：

10.13245/j.hust.201207

中图分类号：

学科分类号：

摘要：

The rolling bearing signal under strong background noise has non-stationary and nonlinear characteristics, resulting in the low accuracy of fault diagnosis.To solve the problem, a method based on singular spectrum decomposition (SSD) and 1D convolutional neural network (1DCNN) was proposed.In this method, the original vibration signal was decomposed into several singular spectrum components (SSC) with different frequency scales by SSD, and the SSC was selected according to the kurtosis criterion to reconstruct the signal.In addition, the 1D (one dimension) convolutional neural network was used to extract the fault feature from the reconstructed signal and obtain diagnosis results.Finally, the experimental results prove the effectiveness and superiority of the proposed method.The diagnosis accuracy is up to 98.9%, which is more accurate and stable than other traditional methods. © 2020, Editorial Board of Journal of Huazhong University of Science and Technology. All right reserved.

引用

页码：38 / 43

页数：5

共 11 条

[1] 9
[2] 1, (2017)
[3] 4
[4] WANG H Q, LI K, SUN H, Et al., Feature extraction method based on Pseudo-Wigner-Ville distribution for rotational machinery in variable operating conditions, Chinese Journal of Mechanical Engineering, 24, 4, pp. 661-668, (2011)
[5] 8, 2
[6] BONIZZI P, KAREL J M H, MESTE O, Et al., Singular spectrum decomposition: a new method for time series decomposition, Advances in Adaptive Data Analysis, 6, 4, (2014)
[7] JIA F, LEI Y, LU N, Et al., Deep normalized convolutional neural network for imbalanced fault classification of machinery and its understanding via visualization, Me- chanical Systems and Signal Processing, 110, pp. 349-367, (2018)
[8] YAN H, BAOPING T, LEI D., Multi-level wavelet packet fusion in dynamic ensemble convolutional neural network for fault diagnosis, Measurement, 127, pp. 246-255, (2018)
[9] INCE T, KIRANYAZ S, EREN L, Et al., Real-time motor fault detection by 1-D convolutional neural networks, IEEE Transactions on Industrial Electronics, 63, 11, pp. 7067-7075, (2016)
[10] 4

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