Coding Feature Extraction and Diagnosis of I.C. Engine Vibration Time-frequency Images

Zhang Shixiong; Cai Yanping; Shi Linsuo; Mou Weijie

doi:10.13433/j.cnki.1003-8728.2018.0310

Volume 37 Issue 3

Mar. 2018

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Article Navigation > Mechanical Science and Technology for Aerospace Engineering > 2018 > 37(3): 391-395

Zhang Shixiong, Cai Yanping, Shi Linsuo, Mou Weijie. Coding Feature Extraction and Diagnosis of I.C. Engine Vibration Time-frequency Images[J]. Mechanical Science and Technology for Aerospace Engineering, 2018, 37(3): 391-395. doi: 10.13433/j.cnki.1003-8728.2018.0310

Citation:

Zhang Shixiong, Cai Yanping, Shi Linsuo, Mou Weijie. Coding Feature Extraction and Diagnosis of I.C. Engine Vibration Time-frequency Images[J]. Mechanical Science and Technology for Aerospace Engineering, 2018, 37(3): 391-395. doi: 10.13433/j.cnki.1003-8728.2018.0310

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Coding Feature Extraction and Diagnosis of I.C. Engine Vibration Time-frequency Images

doi: 10.13433/j.cnki.1003-8728.2018.0310

1.
The Rocket Force Engineering University, Xi'an 710025, China

Received Date: 2017-01-05
Publish Date: 2018-03-05

Abstract

Abstract

According to the problems of parameter selection and feature extraction for vibration diagnosis of traditional internal combustion (I.C) engine, a new fault diagnosis method is discussed. The method based on S-transformation and Module Two Dimensional Principal Components Analysis (M-2DPCA) is proposed to carry out fault diagnosis of I.C. engine valve mechanism. First of all, the method transfers cylinder surface vibration signals of I.C. engine into images through S-transform. Second extracting the optimized projection vectors from the general distribution G which is obtained by all sample sub-images, so that vibration spectrum images can be modularized using M-2DPCA. At last, these features matrix obtained from images project will served as the enters of nearest neighbor classifier, it is used to achieve fault types' division. The method is applied to the diagnosis example of the vibration signal of the valve mechanism eight operating modes, recognition rate up to 94.17%; the effectiveness of the proposed method is proved.
- S-transform,
- M-2DPCA,
- nearest neighbor classifier,
- fault diagnosis

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References(15)

References

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