Proceedings of the 2017 2nd International Conference on Control, Automation and Artificial Intelligence (CAAI 2017)

Construction of Transmission Box Fault Diagnosis System

Authors
Hongbo Zhu, Youkun Zhang, Tongjie Shi
Corresponding Author
Hongbo Zhu
Available Online June 2017.
DOI
10.2991/caai-17.2017.40How to use a DOI?
Keywords
vibration analysis; fault diagnosis; transmission
Abstract

This paper established a set of fault diagnosis system for the fatigue test of the drive axle and transmission. The fault diagnosis system includes two parts: hardware and software. The hardware part is the basis of data acquisition and warning signal output; the software is programmed to realize human-computer interaction, data sampling and storage, signal processing and fault diagnosis on the basis of hardware. In this paper, we introduced a fault diagnosis method based on vibration analysis and the concept of feature vector, according to the feature vector could determine the fault type. Finally, the reliability and practicability of the system are verified by a laboratory test.

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/).

Download article (PDF)

Volume Title
Proceedings of the 2017 2nd International Conference on Control, Automation and Artificial Intelligence (CAAI 2017)
Series
Advances in Intelligent Systems Research
Publication Date
June 2017
ISBN
10.2991/caai-17.2017.40
ISSN
1951-6851
DOI
10.2991/caai-17.2017.40How to use a DOI?
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/).

Cite this article

TY  - CONF
AU  - Hongbo Zhu
AU  - Youkun Zhang
AU  - Tongjie Shi
PY  - 2017/06
DA  - 2017/06
TI  - Construction of Transmission Box Fault Diagnosis System
BT  - Proceedings of the 2017 2nd International Conference on Control, Automation and Artificial Intelligence (CAAI 2017)
PB  - Atlantis Press
SP  - 186
EP  - 189
SN  - 1951-6851
UR  - https://doi.org/10.2991/caai-17.2017.40
DO  - 10.2991/caai-17.2017.40
ID  - Zhu2017/06
ER  -