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

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

Gear Fault Diagnosis Based on Harmonic Wavelet Packet and BP Neural Network

2012 ◽  
Vol 217-219 ◽  
pp. 2683-2687 ◽  
Author(s):  
Chen Jiang ◽  
Xue Tao Weng ◽  
Jing Jun Lou
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Bp Neural Network ◽  
Wavelet Packet ◽  
Processing Technique ◽  
Signal Processing Technique ◽  
Fault Detection And Identification ◽  
Gear Fault ◽  
Harmonic Wavelet ◽  
Gear Fault Diagnosis

The gear fault diagnosis system is proposed based on harmonic wavelet packet transform (WPT) and BP neural network techniques. The WPT is a well-known signal processing technique for fault detection and identification in mechanical system，In the preprocessing of vibration signals, WPT coefficients are used for evaluating their energy and treated as the features to distinguish the fault conditions.In the experimental work, the harmonic wavelets are used as mother wavelets to build and perform the proposed WPT technique. The experimental results showed that the proposed system achieved an average classification accuracy of over 95% for various gear working conditions.

Fault Diagnosis of Gear Box Based on BP Neural Network

2014 ◽  
Vol 667 ◽  
pp. 349-352 ◽  
Author(s):  
Shang Yuan Sun ◽  
Yang Wang
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Bp Neural Network ◽  
Transmission Condition ◽  
Nonlinear Mapping ◽  
Gear Fault ◽  
Fault Diagnosis System ◽  
Gear Fault Diagnosis ◽  
Simulation Results ◽  
Signal Processing Methods

The complexity of gear transmission condition makes a nonlinear mapping relationship between fault form and feature of it, the traditional signal processing methods is not easy to extract fault feature, it has caused great difficulties to gear fault diagnosis. This text is concerned with a class of fault diagnosis system of BP neural network to be used for fault diagnosis of gear. The simulation results show that the method can be used for the identification and diagnosis of gear faults.

Application of RBF Neural Network in Fault Diagnosis for Transmission Gear

2012 ◽  
Vol 433-440 ◽  
pp. 7563-7568 ◽  
Author(s):  
Xi Mei Liu ◽  
Xiao Hui Yao ◽  
Qian Zhao ◽  
Hong Mi Guo
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Bp Neural Network ◽  
Rbf Neural Network ◽  
Broad Application ◽  
Rbf Network ◽  
Gear Fault ◽  
Gear Fault Diagnosis ◽  
Neural Network Toolbox ◽  
Transmission Gear

A method for transmission gearbox fault diagnosis is put forward in this paper by using radial basis function neural network (RBF network). A RBF neural network is created to simulate the gearbox fault diagnosis using Matlab neural network toolbox. Compared with BP neural network, RBF network is superior to the former in accuracy and speed according to the simulate results. This method is accurate and credible in gear fault diagnosis, and it has a broad application prospect in mechanical fault diagnosis.

Gear Fault Diagnosis Method of Intelligence Based on Genetic Algorithm to Optimize the BP Neural Network

2013 ◽  
Vol 756-759 ◽  
pp. 3674-3679 ◽  
Author(s):  
Jie Fang
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Fault Diagnosis ◽  
Bp Neural Network ◽  
Vibration Signal ◽  
Linear Mapping ◽  
Gear Fault ◽  
Gear Fault Diagnosis ◽  
Signal Processing Methods ◽  
Bp Neural Network Model

The work of the gear transmission is very complex, and its failure in the form and features tend to show non-linear mapping. Fault signal is often submerged in conventional vibration signal and noise, it is not easy using traditional signal processing methods to extract fault features which in a difficult to gear fault diagnosis. This paper based on the genetic algorithm to optimize the structure of the BP neural network model for the intelligent diagnosis system which is used in gear fault diagnosis.The experimental results show that this method can be effectively used for the diagnosis and identification of the gears common fault type.

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.

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