New method of fault diagnosis of rotating machinery based on distance of information entropy

doi:10.1007/s11465-011-0124-3

Front Mech Eng

2011, Vol. 6

Issue (2) : 249-253 https://doi.org/10.1007/s11465-011-0124-3

RESEARCH ARTICLE

New method of fault diagnosis of rotating machinery based on distance of information entropy

Houjun SU(

), Tielin SHI, Fei CHEN, Shuhong HUANG

School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan 430074, China

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Abstract

This paper introduces the basic conception of information fusion and some fusion diagnosis methods commonly used nowadays in rotating machinery. From the thought of the information fusion, a new quantitative feature index monitoring and diagnosing the vibration fault of rotating machinery, which is called distance of information entropy, is put forward on the basis of the singular spectrum entropy in time domain, power spectrum entropy in frequency domain, wavelet energy spectrum entropy, and wavelet space feature entropy in time-frequency domain. The mathematic deduction suggests that the conception of distance of information entropy is accordant with the maximum subordination principle in the fuzzy theory. Through calculation it has been proved that this method can effectively distinguish different fault types. Then, the accuracy of rotor fault diagnosis can be improved through the curve chart of the distance of information entropy at multi-speed.

Keywords rotating machinery information fusion fault diagnosis Information entropy distance of the information entropy

Corresponding Author(s): SU Houjun,Email:huagongdashj@163.com

Issue Date: 05 June 2011

Cite this article:

Houjun SU,Tielin SHI,Fei CHEN, et al. New method of fault diagnosis of rotating machinery based on distance of information entropy[J]. Front Mech Eng, 2011, 6(2): 249-253.

URL:

https://academic.hep.com.cn/fme/EN/10.1007/s11465-011-0124-3
https://academic.hep.com.cn/fme/EN/Y2011/V6/I2/249

Tab.1 Six fault entropy points in four-dimension space

Fig.1 Entropy distance curve of RI fault

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