A gear fault diagnosis method based on manifold semi-supervised K-means clustering algorithm

2019 ◽  
Author(s):  
Wenxing Zhang ◽  
Pengfei Zhao ◽  
Jianguo Wang ◽  
Wenjing Liu
Keyword(s):  
Fault Diagnosis ◽  
Clustering Algorithm ◽  
Gear Fault ◽  
Diagnosis Method ◽  
Gear Fault Diagnosis

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

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.

A Gear Fault Diagnosis Method Based on EEMD Cloud Model and PSO_SVM

2021 ◽  
Author(s):  
Yunhui Ou ◽  
Darong Huang ◽  
Chong Hu ◽  
Haiyang Hao ◽  
Jiaxing Gong ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Cloud Model ◽  
Gear Fault ◽  
Diagnosis Method ◽  
Gear Fault Diagnosis

scholarly journals Gear Fault Diagnosis Based on Kurtosis Criterion VMD and SOM Neural Network

2019 ◽  
Vol 9 (24) ◽  
pp. 5424 ◽  
Author(s):  
Dongming Xiao ◽  
Jiakai Ding ◽  
Xuejun Li ◽  
Liangpei Huang
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Characteristic Curve ◽  
Vibration Signal ◽  
Good Effect ◽  
Feature Vectors ◽  
Gear Fault ◽  
Diagnosis Method ◽  
Som Neural Network ◽  
Gear Fault Diagnosis

A gear fault diagnosis method based on kurtosis criterion variational mode decomposition (VMD) and self-organizing map (SOM) neural network is proposed. Firstly, the VMD algorithm is used to decompose the gear vibration signal, and the instantaneous frequency mean is calculated as the evaluation index, and the characteristic curve is drawn to screen out the most relevant intrinsic mode functions (IMFs) of the original vibration signal. Then, the number of VMD decompositions is determined, and the kurtosis value of IMFs are extracted to form the feature vectors. Then, the kurtosis value feature vectors of IMFs are normalized to form the kurtosis value normalized vectors. Finally, the normalized vectors of kurtosis value are input into SOM neural network to realize gear fault diagnosis. When the number of training times of SOM neural network is 100, the gear fault category is accurately classified by SOM neural network. The results show that when the training times of SOM neural network is 100 times, the gear fault diagnosis method, based on the kurtosis criterion VMD and SOM neural network is 100%, which indicates that the new method has a good effect on gear fault diagnosis.

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