Research on Fault Diagnosis Model of Rotating Machinery Vibration Based on Information Entropy and Improved SVM Article Swipe

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Hankun Bing , Yuzhu Zhao , Le Pang , Minmin Zhao · YOU? · · 2019 · Open Access · · DOI: https://doi.org/10.1051/e3sconf/201911802036

Based on the concept of information entropy, this paper analyzes typical nonlinear vibration fault signals of steam turbine based on spectrum, wavelet and HHT theory methods, and extracts wavelet energy spectrum entropy, IMF energy spectrum entropy, time domain singular value entropy and frequency domain power spectrum entropy as faults. The feature is supported by a support vector machine (SVM) as a learning platform. The research results show that the fusion information entropy describes the vibration fault more comprehensively, and the support vector machine fault diagnosis model can achieve higher diagnostic accuracy.

Type

article

Language

en

Landing Page

https://doi.org/10.1051/e3sconf/201911802036

PDF

https://www.e3s-conferences.org/articles/e3sconf/pdf/2019/44/e3sconf_icaeer18_02036.pdf

OA Status

diamond

Cited By

3

References

3

Related Works

10

OpenAlex ID

https://openalex.org/W2979084644

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