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

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 90.75%, 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.

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

Sensors ◽

10.3390/s18030782 ◽

2018 ◽

Vol 18 (3) ◽

pp. 782 ◽

Cited By ~ 35

Author(s):

Moshen Kuai ◽

Gang Cheng ◽

Yusong Pang ◽

Yong Li

Keyword(s):

Fault Diagnosis ◽

Planetary Gear ◽

Permutation Entropy ◽

Gear Fault ◽

Gear Fault Diagnosis

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

Proceedings of the 2017 2nd International Conference on Electrical, Automation and Mechanical Engineering (EAME 2017) ◽

10.2991/eame-17.2017.36 ◽

2017 ◽

Author(s):

Yong Li ◽

Gang Cheng ◽

Xihui Chen ◽

Chang Liu

Keyword(s):

Neural Network ◽

Fault Diagnosis ◽

Bp Neural Network ◽

Planetary Gear ◽

Gear Fault ◽

Gear Fault Diagnosis

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Motor current signal analysis using deep neural networks for planetary gear fault diagnosis

Measurement ◽

10.1016/j.measurement.2019.05.074 ◽

2019 ◽

Vol 145 ◽

pp. 45-54 ◽

Cited By ~ 10

Author(s):

Feng Li ◽

Xinyu Pang ◽

Zhaojian Yang

Keyword(s):

Neural Networks ◽

Fault Diagnosis ◽

Signal Analysis ◽

Deep Neural Networks ◽

Planetary Gear ◽

Current Signal ◽

Motor Current ◽

Gear Fault ◽

Gear Fault Diagnosis

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Gear Fault Diagnosis Method Based on Local Characteristic-Scale Decomposition Multi-Scale Permutation Entropy and Radial Basis Function Network

Journal of Computational and Theoretical Nanoscience ◽

10.1166/jctn.2017.6917 ◽

2017 ◽

Vol 14 (10) ◽

pp. 5054-5063 ◽

Cited By ~ 4

Author(s):

DucHieu Le ◽

Junsheng Cheng ◽

Yu Yang ◽

Minhhieu Pham ◽

VanTrong Thai

Keyword(s):

Fault Diagnosis ◽

Radial Basis Function Network ◽

Permutation Entropy ◽

Local Characteristic ◽

Characteristic Scale ◽

Multi Scale ◽

Gear Fault ◽

Diagnosis Method ◽

Gear Fault Diagnosis ◽

Function Network

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Variable cosine windowing of intrinsic mode functions: Application to gear fault diagnosis

Measurement ◽

10.1016/j.measurement.2011.11.001 ◽

2012 ◽

Vol 45 (3) ◽

pp. 415-426 ◽

Cited By ~ 23

Author(s):

Anand Parey ◽

Ram Bilas Pachori

Keyword(s):

Fault Diagnosis ◽

Intrinsic Mode Functions ◽

Gear Fault ◽

Gear Fault Diagnosis

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Study on planetary gear fault diagnosis based on variational mode decomposition and deep neural networks

Measurement ◽

10.1016/j.measurement.2018.08.002 ◽

2018 ◽

Vol 130 ◽

pp. 94-104 ◽

Cited By ~ 35

Author(s):

Yong Li ◽

Gang Cheng ◽

Chang Liu ◽

Xihui Chen

Keyword(s):

Neural Networks ◽

Fault Diagnosis ◽

Deep Neural Networks ◽

Planetary Gear ◽

Variational Mode Decomposition ◽

Mode Decomposition ◽

Gear Fault ◽

Gear Fault Diagnosis

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APPLICATION OF A CONTINUOUS WAVELET TRANSFORMS AND ANFIS TO FAULT DIAGNOSIS OF A GEARBOX OF TRACTOR

Journal of Mechanical Engineering ◽

10.3329/jme.v46i1.32517 ◽

2017 ◽

Vol 46 (1) ◽

pp. 9-15

Author(s):

Mostafa Bahrami ◽

Hossein Javadikia ◽

Ebrahim Ebrahimi

Keyword(s):

Fault Diagnosis ◽

Wavelet Transforms ◽

Fuzzy Inference ◽

Vibration Signal ◽

Continuous Wavelet ◽

Inference System ◽

Continuous Wavelet Transforms ◽

Gearbox Vibration ◽

Gear Fault ◽

Tractor Gear

This study proposes a technique based on continuous wavelet transform added by adaptive-neural-fuzzy inference system (ANFIS) for fault diagnosis of gearbox of MF285 tractor. Varying fault can cause changes in a measured gearbox vibration signal. A gear motion vibration signal in speed of 1500 r/min is analyzed as source data. Energy of coefficients wavelet is proposed to evaluate tractor gear fault by ANFIS. We found that this technique can correctly indicate early gear fault.

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Planetary gear fault diagnosis based on instantaneous angular speed analysis

2017 23rd International Conference on Automation and Computing (ICAC) ◽

10.23919/iconac.2017.8081996 ◽

2017 ◽

Cited By ~ 1

Author(s):

Qiang Zeng ◽

Mones Zainab ◽

Yimin Shao ◽

Fengshou Gu ◽

Andrew D. Ball

Keyword(s):

Fault Diagnosis ◽

Planetary Gear ◽

Angular Speed ◽

Instantaneous Angular Speed ◽

Gear Fault ◽

Gear Fault Diagnosis

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Gear Fault Diagnosis Method Using EEMD Sample Entropy and Grey Incidence

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.694-697.1151 ◽

2013 ◽

Vol 694-697 ◽

pp. 1151-1154

Author(s):

Wen Bin Zhang ◽

Ya Song Pu ◽

Jia Xing Zhu ◽

Yan Ping Su

Keyword(s):

Fault Diagnosis ◽

Ensemble Empirical Mode Decomposition ◽

Sample Entropy ◽

Morphological Filter ◽

Intrinsic Mode Functions ◽

Vibration Signals ◽

Mode Decomposition ◽

Gear Fault ◽

Diagnosis Method ◽

Gear Fault Diagnosis

In this paper, a novel fault diagnosis method for gear was approached based on morphological filter, ensemble empirical mode decomposition (EEMD), sample entropy and grey incidence. Firstly, in order to eliminate the influence of noises, the line structure element was selected for morphological filter to denoise the original signal. Secondly, denoised vibration signals were decomposed into a finite number of stationary intrinsic mode functions (IMF) and some containing the most dominant fault information were calculated the sample entropy. Finally, these sample entropies could serve as the feature vectors, the grey incidence of different gear vibration signals was calculated to identify the fault pattern and condition. Practical results show that this method can be used in gear fault diagnosis effectively.

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