Maximum envelope-based Autogram and Symplectic geometry mode decomposition based gear fault diagnosis method

Measurement ◽  
2020 ◽  
pp. 108575
Author(s):  
Xinglong Wang ◽  
Jinde Zheng ◽  
Haiyang Pan ◽  
Qingyun Liu ◽  
Chengjun Wang
Keyword(s):  
Fault Diagnosis ◽  
Symplectic Geometry ◽  
Mode Decomposition ◽  
Gear Fault ◽  
Diagnosis Method ◽  
Gear Fault Diagnosis

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.

Gear Fault Diagnosis Methods Based on EMD and HMM

2013 ◽  
Vol 333-335 ◽  
pp. 1684-1687
Author(s):  
Bin Wu ◽  
Song He Zhang ◽  
Yue Gang Luo ◽  
Shan Ping Yu
Keyword(s):  
Fault Diagnosis ◽  
Vibration Signal ◽  
Modulation Amplitude ◽  
Double Frequency ◽  
Gear Mesh ◽  
Gear Fault ◽  
Diagnosis Method ◽  
Modulation Signal ◽  
Gear Fault Diagnosis ◽  
Modulation Sideband

Due to the feature and the forms of motion of the gears, the vibration signal of the gear is mainly the frequency modulation, amplitude modulation, or hybrid modulation signal corresponding to the gear-mesh frequency and its double frequency signal. When faults arise on the gears, the number and shape of the modulation sideband will be changed. The structures and forms of the FM composition differ according to the type of faults. According to the above mentioned characteristic, this essay raises a method to disassemble the gear vibrate signal, points out the formulas to build up characteristic vector, on that basis, the essay raised a gear fault diagnosis method based on EMD and Hidden Markov Model (HMM), this method can identify the working condition of the normal gears, snaggletooth gears, and pitting gears.

A Novel Marine Gear Fault Diagnosis Method Based on VMD and MDE

2021 ◽  
Author(s):  
Xiaolong Zhou ◽  
Gang Liu ◽  
Xianliang Liu ◽  
Weidong Zhang
Keyword(s):  
Fault Diagnosis ◽  
Gear Fault ◽  
Diagnosis Method ◽  
Gear Fault Diagnosis

scholarly journals Fault Feature Extraction and Diagnosis of Gearbox Based on EEMD and Deep Briefs Network

2017 ◽  
Vol 2017 ◽  
pp. 1-10 ◽  
Author(s):  
Kai Chen ◽  
Xin-Cong Zhou ◽  
Jun-Qiang Fang ◽  
Peng-fei Zheng ◽  
Jun Wang
Keyword(s):  
Fault Diagnosis ◽  
Original Data ◽  
Ensemble Empirical Mode Decomposition ◽  
Intrinsic Mode Functions ◽  
Mode Decomposition ◽  
Coupling System ◽  
Reconstructed Signal ◽  
Gear Transmission System ◽  
Gear Fault ◽  
Diagnosis Method

A gear transmission system is a complex nonstationary and nonlinear time-varying coupling system. When faults occur on gear system, it is difficult to extract the fault feature. In this paper, a novel fault diagnosis method based on ensemble empirical mode decomposition (EEMD) and Deep Briefs Network (DBN) is proposed to treat the vibration signals measured from gearbox. The original data is decomposed into a set of intrinsic mode functions (IMFs) using EEMD, and then main IMFs were chosen for reconstructed signal to suppress abnormal interference from noise. The reconstructed signals were regarded as input of DBN to identify gearbox working states and fault types. To verify the effectiveness of the EEMD-DBN in detecting the faults, a series of gear fault simulate experiments at different states were carried out. Results showed that the proposed method which coupled EEMD and DBN can improve the accuracy of gear fault identification and it is capable of applying to fault diagnosis in practical application.

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