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Open AccessArticle

Research of Planetary Gear Fault Diagnosis Based on Permutation Entropy of CEEMDAN and ANFIS

1
School of Mechatronic Engineering, China University of Mining and Technology, Xuzhou 221116, China
2
Faculty Mechanical, Maritime and Materials Engineering, Delft University of Technology, Delft 2628 CD, The Netherlands
*
Author to whom correspondence should be addressed.
Sensors 2018, 18(3), 782; https://doi.org/10.3390/s18030782
Received: 8 January 2018 / Revised: 19 February 2018 / Accepted: 27 February 2018 / Published: 5 March 2018
(This article belongs to the Special Issue Sensors for Fault Detection)
For planetary gear has the characteristics of small volume, light weight and large transmission ratio, it is widely used in high speed and high power mechanical system. Poor working conditions result in frequent failures of planetary gear. A method is proposed for diagnosing faults in planetary gear based on permutation entropy of Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) Adaptive Neuro-fuzzy Inference System (ANFIS) in this paper. The original signal is decomposed into 6 intrinsic mode functions (IMF) and residual components by CEEMDAN. Since the IMF contains the main characteristic information of planetary gear faults, time complexity of IMFs are reflected by permutation entropies to quantify the fault features. The permutation entropies of each IMF component are defined as the input of ANFIS, and its parameters and membership functions are adaptively adjusted according to training samples. Finally, the fuzzy inference rules are determined, and the optimal ANFIS is obtained. The overall recognition rate of the test sample used for ANFIS is 90%, and the recognition rate of gear with one missing tooth is relatively high. The recognition rates of different fault gears based on the method can also achieve better results. Therefore, the proposed method can be applied to planetary gear fault diagnosis effectively. View Full-Text
Keywords: planetary gear; fault diagnosis; CEEMDAN; permutation entropy; ANFIS planetary gear; fault diagnosis; CEEMDAN; permutation entropy; ANFIS
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MDPI and ACS Style

Kuai, M.; Cheng, G.; Pang, Y.; Li, Y. Research of Planetary Gear Fault Diagnosis Based on Permutation Entropy of CEEMDAN and ANFIS. Sensors 2018, 18, 782.

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