Research on Fault Diagnosis of Rotating Machinery of Vehicle Transmission

DOI

10.2991/mecae-17.2017.56How to use a DOI?

Keywords

Special Armored Vehicle Gearbox, Fault Diagnosis, EMD, Endpoint Effect, Feature Extraction, SVM

Abstract

In this paper, the gearbox as the research object, under the conditions of strong interference signal acquisition and analysis, denoising, eigenvalue extraction, pattern recognition and fault prediction has very important practical significance. Aiming at the problem of end effect in Empirical Mode Decomposition (EMD), an improved method for the continuation of the even-extended cosine window function (EECW) of signal sequences is proposed. Firstly, the signal sequence is continually extended to realize the smooth transition between the extension data and the original signal, avoiding the jump of the instantaneous frequency of the signal. Secondly, there is the problem of continuation error for this extension method. (Or slow speed) to the internal development of data to ensure the correct decomposition of the signal valid data to improve the accuracy of the decomposition of the signal to achieve the improvement of EMD algorithm. Through the simulation analysis and fault diagnosis, it is shown that the method can effectively suppress EMD endpoint effect and realize the effective diagnosis of rotating machinery fault. The experimental results show that the proposed method has high precision and good performance

Copyright

© 2017, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Tao Zhang
AU  - Huiyuan Xue
PY  - 2017/03
DA  - 2017/03
TI  - Research on Fault Diagnosis of Rotating Machinery of Vehicle Transmission
BT  - Proceedings of the 2017 International Conference on Mechanical, Electronic, Control and Automation Engineering (MECAE 2017)
PB  - Atlantis Press
SP  - 298
EP  - 305
SN  - 2352-5401
UR  - https://doi.org/10.2991/mecae-17.2017.56
DO  - 10.2991/mecae-17.2017.56
ID  - Zhang2017/03
ER  -