Study of Planetary Gear Fault Diagnosis Based on Energy of LMD and BP Neural Network

DOI

10.2991/eame-17.2017.36How to use a DOI?

Keywords

LMD; fault diagnosis; planetary gear; energy; BP neural network

Abstract

Planetary gear box has the characteristics of small volume and large transmission ratio and is widely used in construction machinery. After a long period of operation, the gear fault occurs frequently, which has a great influence on the equipment. However, due to its complex structure, the fault signal is often submerged in the inherent signal of the gearbox. In order to extract the fault feature from the signal, a method based on energy of Local mean decomposition (LMD) and Back Propagation (BP) neural network is proposed to solve this problem in this paper. Original signal is decomposed by LMD into 6 product functions (PF). The energy of each PF component are calculated and defined as the input of the BP neural network. Optimal model of neural network can be obtained based on sample training. The result of experimental shows that the proposed method can achieve an overall recognition rate of 95.5%, which proves that it is an effective method for planetary gear fault diagnosis.

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  - Yong Li
AU  - Gang Cheng
AU  - Xihui Chen
AU  - Chang Liu
PY  - 2017/04
DA  - 2017/04
TI  - Study of Planetary Gear Fault Diagnosis Based on Energy of LMD and BP Neural Network
BT  - Proceedings of the 2017 2nd International Conference on Electrical, Automation and Mechanical Engineering (EAME 2017)
PB  - Atlantis Press
SP  - 146
EP  - 150
SN  - 2352-5401
UR  - https://doi.org/10.2991/eame-17.2017.36
DO  - 10.2991/eame-17.2017.36
ID  - Li2017/04
ER  -