Rotating machinery fault classification method using multi-sensor feature extraction and fusion

被引：0

作者：

Zhang Q. ^{[1
]}

Wen C. ^{[2
]}

机构：

[1] College of Electrical Engineering, Henan University of Technology, Zhengzhou

[2] School of Automation, Hangzhou Dianzi University, Hangzhou

来源：

International Journal of Performability Engineering | 2020年 / 16卷 / 04期

基金：

中国国家自然科学基金;

关键词：

Data preprocessing; Direct fusion; Fault classification; Merge fusion; Multi-sensor; Rotating machinery; Weighted fusion;

D O I：

10.23940/ijpe.20.04.p9.577586

中图分类号：

学科分类号：

摘要：

This paper focuses on data information preprocessing methods in the fault classification of rotating machinery. In order to avoid the information loss caused by the weighted fusion method, a merging fusion method is provided to obtain the final feature information. Furthermore, a direct fusion method that synchronizes the extraction and fusion of multi-sensor feature information is also proposed. The artificial neural network is used to test the three proposed information preprocessing methods and obtain rotary machinery fault classification methods. A final comparative experiment is given to compare the three methods proposed above in the fault classification of rotating machinery. © 2020 Totem Publisher, Inc. All rights reserved.

引用

页码：577 / 586

页数：9

共 18 条

[1]

Wang G.B., He Z.J., Chen X.F., Lai Y.N., Basic research on machinery fault diagnosis-what is the prescriptio, Journal of Mechanical Engineering, 1, 49, pp. 63-72, (2013)

[2]

Liu Y., Xu L.X., Ma C.X., Yang D.Y., High-performance back propagation neural network algorithm for classification of mass load data, Electrical Measurement and Instrumentation, 17, 56, pp. 88-95, (2019)

[3]

Yang X.M., Research and analysis of fault diagnosis methods for rotating machinery, Shandong Industrial Technology, 10, (2017)

[4]

Jin X.H., Sun Y., Shan J.H., Wu G.Y., Fault diagnosis and prognosis for wind turbines: An overview, Chinese Journal of Scientific Instrument, 5, 38, pp. 1041-1053, (2017)

[5]

Lu X.J., Liao S., Wavelet packets decompose of vibration signal for rotating machinery and its fault testing, Chinese Journal of Scientific Instrument, 5, pp. 484-487, (2002)

[6]

Zhao F.Z., Yang R.G., Voltage sag disturbance detection based on short time fourier transform, Proceedings of the Chinese Society for Electrical Engineering, 10, pp. 28-34, (2007)

[7]

Xiang L., Tang G.J., Hu A.J., Vibration signal's time-frequency analysis and comparison for a rotating machinery, Journal of Vibration and Shock, 2, 29, pp. 42-45, (2010)

[8]

Liao Y.X., Yang J., Intelligent fault diagnosis method and research of mechanical equipment: Taking rotating machinery as an example, Value Engineering, 22, 38, pp. 224-226, (2019)

[9]

Xue X.M., Zhou J.Z., A hybrid fault diagnosis approach based on mixed-domain state features for rotating machinery, ISA Transactions, 66, pp. 284-295, (2017)

[10]

Li X.J., Yang D.L., Guo D.T., Jiang L.L., Fault diagnosis method based on multi-sensors installed on the base and KPCA, Chinese Journal of Scientific Instrument, 7, 32, pp. 1551-1557, (2011)

← 1 2 →