scholarly journals Gear Fault Diagnosis Method Using Ensemble Empirical Mode Decomposition Energy Distribution and Grey Similar Incidence

2014 ◽  
Vol 50 (7) ◽  
pp. 70 ◽  
Author(s):  
Wenbin ZHANG
Keyword(s):  
Fault Diagnosis ◽  
Energy Distribution ◽  
Empirical Mode Decomposition ◽  
Ensemble Empirical Mode Decomposition ◽  
Mode Decomposition ◽  
Gear Fault ◽  
Diagnosis Method ◽  
Gear Fault Diagnosis ◽  
Similar Incidence

scholarly journals Gear Fault Diagnosis Based on VMD Sample Entropy and Discrete Hopfield Neural Network

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Jiakai Ding ◽  
Dongming Xiao ◽  
Liangpei Huang ◽  
Xuejun Li
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Empirical Mode Decomposition ◽  
Hopfield Neural Network ◽  
Sample Entropy ◽  
Feature Vectors ◽  
Mode Decomposition ◽  
Gear Fault ◽  
Diagnosis Method ◽  
Gear Fault Diagnosis

The gear fault signal has some defects such as nonstationary nonlinearity. In order to increase the operating life of the gear, the gear operation is monitored. A gear fault diagnosis method based on variational mode decomposition (VMD) sample entropy and discrete Hopfield neural network (DHNN) is proposed. Firstly, the optimal VMD decomposition number is selected by the instantaneous frequency mean value. Then, the sample entropy value of each intrinsic mode function (IMF) is extracted to form the gear feature vectors. The gear feature vectors are coded and used as the memory prototype and memory starting point of DHNN, respectively. Finally, the coding vector is input into DHNN to realize fault pattern recognition. The newly defined coding rules have a significant impact on the accuracy of gear fault diagnosis. Driven by self-associative memory, the coding of gear fault is accurately classified by DHNN. The superiority of the VMD-DHNN method in gear fault diagnosis is verified by comparing with an advanced signal processing algorithm. The results show that the accuracy based on VMD sample entropy and DHNN is 91.67% of the gear fault diagnosis method. The experimental results show that the VMD method is better than the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and empirical mode decomposition (EMD), and the effect of it in the diagnosis of gear fault diagnosis is emphasized.

Gear Fault Diagnosis Method Using EEMD Sample Entropy and Grey Incidence

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.

Gear Fault Diagnosis Method Based on the Integration of Empirical Mode Decomposition and Cepstrum Analysis

2013 ◽  
Vol 310 ◽  
pp. 328-333 ◽  
Author(s):  
Bing Luo ◽  
Wen Tong Yang ◽  
Zhi Feng Liu ◽  
Yong Sheng Zhao ◽  
Li Gang Cai
Keyword(s):  
Fault Diagnosis ◽  
Empirical Mode Decomposition ◽  
Vibration Signal ◽  
Mechanical Transmission ◽  
Intrinsic Mode Functions ◽  
Cepstrum Analysis ◽  
Mode Decomposition ◽  
Gear Fault ◽  
Diagnosis Method ◽  
Gear Fault Diagnosis

Gear is the most common mechanical transmission equipment. Therefore, gear fault diagnosis is of much significance. In this article, a gear fault diagnosis method based on the integration of empirical mode decomposition and cepstrum is proposed by introducing empirical mode decomposition and cepstrum into gear fault analysis. Firstly EMD is used to decompose the gear vibration signal finite number of intrinsic mode functions and a residual error item. To do gear fault diagnosis, cepstrum analysis is carried upon those intrinsic mode functions to extract feature information from the vibration signal. The results of the study on simulated and experimental signals show that this method is better than the cepstrum method and it can precisely locate the site of gear failure.

Application of MSE-EEMD Method in Gear Fault Diagnosis

2014 ◽  
Vol 530-531 ◽  
pp. 261-265
Author(s):  
Min Qiang Xu ◽  
Yong Bo Li ◽  
Hai Yang Zhao ◽  
Si Yang Zhang
Keyword(s):  
Fault Diagnosis ◽  
Empirical Mode Decomposition ◽  
Engineering Application ◽  
Vibration Signal ◽  
Ensemble Empirical Mode Decomposition ◽  
Multiscale Entropy ◽  
Mode Decomposition ◽  
Gear Fault ◽  
Gear Fault Diagnosis ◽  
Eemd Method

Focus on the nonlinear and non-stationary characteristics of gear box vibration signal, the method of gear fault diagnosis based on Ensemble Empirical Mode Decomposition (EEMD) and multiscale entropy (MSE) was proposed . The complicated signal can be decomposed into several stationary IMF components with reality meanings by EEMD which has the advantages of eliminating aliasing state of vibration signal, and the MSE can extract the fault feature from the signals effectively. The concepts of EEMD and MSE are introduced firstly, and then they are applied to measure the complexity of gearbox signals. Through the engineering application of the diagnosis on gear typical fault of different wearing degree demonstrated that the proposed method can extracting the fault feature of gear fault effectively and realize the gear fault diagnosis.

A fault diagnosis method of rolling element bearings based on CEEMDAN

2015 ◽  
Vol 231 (10) ◽  
pp. 1804-1815 ◽  
Author(s):  
Yaguo Lei ◽  
Zongyao Liu ◽  
Julien Ouazri ◽  
Jing Lin
Keyword(s):  
Fault Diagnosis ◽  
Empirical Mode Decomposition ◽  
Ensemble Empirical Mode Decomposition ◽  
Rolling Element Bearings ◽  
Intrinsic Mode Functions ◽  
Residual Noise ◽  
Mode Decomposition ◽  
Rolling Element ◽  
Noisy Signals ◽  
Diagnosis Method

Ensemble empirical mode decomposition (EEMD) represents a valuable aid in empirical mode decomposition (EMD) and has been widely used in fault diagnosis of rolling element bearings. However, the intrinsic mode functions (IMFs) generated by EEMD often contain residual noise. In addition, adding different white Gaussian noise to the signal to be analyzed probably produces a different number of IMFs, and different number of IMFs makes difficult the averaging. To alleviate these two drawbacks, complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) was previously presented. Utilizing the advantages of CEEMDAN in extracting weak characteristics from noisy signals, a new fault diagnosis method of rolling element bearings based on CEEMDAN is proposed. With this method, a particular noise is added at each stage and after each IMF extraction, a unique residue is computed. In this way, this method solves the problem of the final averaging and obtains IMFs with less noise. A simulated signal is used to illustrate the effectiveness of the proposed method, and the decomposition results show that the method obtains more accurate IMFs than the EEMD. To further demonstrate the proposed method, it is applied to fault diagnosis of locomotive rolling element bearings. The diagnosis results prove that the method based on CEEMDAN may reveal the fault characteristic information of rolling element bearings better.

Research on Fault Diagnosis for Gear Based on Ensemble Empirical Mode Decomposition and Slice Bi-Spectrum

2013 ◽  
Vol 718-720 ◽  
pp. 934-939
Author(s):  
Gui Ji Tang ◽  
Xiao Long Wang
Keyword(s):  
Fault Diagnosis ◽  
Empirical Mode Decomposition ◽  
Noise Suppression ◽  
Ensemble Empirical Mode Decomposition ◽  
Quadratic Phase ◽  
Mode Decomposition ◽  
Gear Fault ◽  
Envelope Signal ◽  
Harmonic Components ◽  
Simulation Signal

A new method on fault diagnosis for gear based on ensemble empirical mode decomposition and slice bi-spectrum is proposed. Firstly, fault signal was decomposed into a series of intrinsic mode function components of different frequency bands by EEMD, and then calculated the envelope signal of IMF component by Hilbert demodulation method. Finally, analyzed the envelope signal by slice bi-spectrum and extracted the fault characteristic frequency. The anti-alias decomposition capacity of EEMD and capabilities of noise suppression and non-quadratic phase coupling harmonic components elimination of slice bi-spectrum were verified by analyzing the simulation signal. The analysis results of gear pitting failure signal and gear wear fault signal showed that this method could judge gear fault type accurately and has a certainly degree reliability.

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