ITM Web Conf.
Volume 11, 20172017 International Conference on Information Science and Technology (IST 2017)
|Number of page(s)||9|
|Section||Session VIII: Signal Processing|
|Published online||23 May 2017|
Fault Diagnosis of Planetary Gear Based on Fuzzy Entropy of CEEMDAN and MLP Neural Network by Using Vibration Signal
School of Mechatronic Engineering, China University of Mining and Technology, Xuzhou, 221116, China
a Corresponding author: email@example.com
A method of planetary gear fault diagnosis based on the fuzzy entropy of complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and multi-layer perceptron (MLP) neural network is proposed. The vibration signal is decomposed into multiple intrinsic mode functions (IMFs) by CEEMDAN, and the fuzzy entropy that combines the fuzzy function and sample entropy is proposed and used to extract the feature information contained in each IMF. The fuzzy entropies of each IMF are defined as the input of the MLP neural network, and the planetary gear status can be recognized by the output. The experiments prove the proposed method is effective.
© Owned by the authors, published by EDP Sciences, 2017
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