scholarly journals Compound Fault Diagnosis of Gearbox Based on RLMD and SSA-PNN

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Shitong Liang ◽  
Jie Ma
Keyword(s):  
Fault Diagnosis ◽  
Feature Vector ◽  
Search Algorithm ◽  
Probabilistic Neural Network ◽  
Permutation Entropy ◽  
Diagnosis Method ◽  
Diagnosis Accuracy ◽  
Local Mean ◽  
Fault Information

In order to solve the difficulty in the classification of gearbox compound faults, a gearbox fault diagnosis method based on the sparrow search algorithm (SSA) improved probabilistic neural network (PNN) is proposed. Firstly, the gearbox fault signal is decomposed into a series of product functions (PFs) by robust local mean decomposition (RLMD). Then, the permutation entropy of PFs, which contains much fault information, is calculated to construct the feature vector and input it into the SSA-PNN model. The experimental results show that compared with the traditional fault diagnosis methods based on EMD-BP and EEMD-PNN, the gearbox fault diagnosis method based on RLMD and SSA-PNN has higher diagnosis accuracy.

scholarly journals Compound Fault Diagnosis of Rolling Bearing Based on ALIF-KELM

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Jie Ma ◽  
Shitong Liang ◽  
Zhengyu Du ◽  
Ming Chen
Keyword(s):  
Fault Diagnosis ◽  
Rolling Bearing ◽  
Fault Classification ◽  
Training Set ◽  
Sample Data ◽  
Diagnosis Method ◽  
Main Fault ◽  
Bp Model ◽  
Fault Information

Aiming at the shortcomings of difficult classification of rolling bearing compound faults and low recognition accuracy, a composite fault diagnosis method of rolling bearing combined with ALIF and KELM is proposed. First, the basic concepts of ALIF and KELM are introduced, and then ALIF is used to decompose the sample data of vibration signals of different bearing states so that each sample can get several IMFs, select the top K IMFs containing the main fault information from each sample, calculate the energy feature and sample entropy of each IMF, and construct a fault feature vector with a dimension of 2K. Finally, the feature vectors of the training set and the test set are input into the KELM model for fault classification. Experimental results show that, compared with EMD-KELM model, ALIF-ELM model, ALIF-BP model, and IFD-KELM model, the rolling bearing composite fault diagnosis method based on the ALIF-KELM model has higher classification accuracy.

A New Fault Diagnosis Method of Adaptive Demodulated Resonance Technique Based on Wavelet Packet in Multi-Information Domains

2012 ◽  
Vol 562-564 ◽  
pp. 1598-1601
Author(s):  
Cai Qin Liu ◽  
Er Ling Cao ◽  
Sheng Qiang Wu
Keyword(s):  
Fault Diagnosis ◽  
Feature Vector ◽  
Wavelet Packet ◽  
Center Frequency ◽  
Hydraulic Pump ◽  
Resonance Technique ◽  
Diagnosis Method ◽  
Information Domain ◽  
Axial Piston ◽  
Fault Information

Fault information is incomplete while using a single information domain fault feature parameters to construct fault feature vector, and demodulated resonance technique have to predetermine resonant frequency and fixed center frequency also has its shortcomings , in order to solve these problems, a new fault diagnosis method is proposed of adaptive demodulated resonance technique based on wavelet packet in multi-information domains. The fault feature vector extracted from multi- information domains is described, signal processing flow of envelope demodulation based on denoising and filtering of wavelet packet is analyzed, the fault diagnosis method of adaptive demodulated resonance technique based on wavelet packet is given, and the method is applied to fault diagnosis of axial piston hydraulic pump. Experiment results show that multi-domain feature vector increases the completeness of the fault information, it is able to obtain good diagnosis effect, and the new fault diagnosis method is able to identify known and unknown faults resonance frequency automatically, the frequency range is narrow, the rate of diagnosis is high.

scholarly journals A Novel Intelligent Method for Bearing Fault Diagnosis Based on EEMD Permutation Entropy and GG Clustering

2020 ◽  
Vol 10 (1) ◽  
pp. 386 ◽  
Author(s):  
Jingbao Hou ◽  
Yunxin Wu ◽  
Hai Gong ◽  
A. S. Ahmad ◽  
Lei Liu
Keyword(s):  
Fault Diagnosis ◽  
Clustering Algorithm ◽  
Feature Vector ◽  
Vibration Signal ◽  
Rolling Bearing ◽  
Permutation Entropy ◽  
Linear Discriminant ◽  
Bearing Fault ◽  
Combination Methods ◽  
Diagnosis Method

For a rolling bearing fault that has nonlinearity and nonstationary characteristics, it is difficult to identify the fault category. A rolling bearing clustering fault diagnosis method based on ensemble empirical mode decomposition (EEMD), permutation entropy (PE), linear discriminant analysis (LDA), and the Gath–Geva (GG) clustering algorithm is proposed. Firstly, we decompose the vibration signal using EEMD, and several inherent modal components are obtained. Then, the permutation entropy values of each modal component are calculated to get the entropy feature vector, and the entropy feature vector is reduced by the LDA method to be used as the input of the clustering algorithm. The data experiments show that the proposed fault diagnosis method can obtain satisfactory clustering indicators. It implies that compared with other mode combination methods, the fault identification method proposed in this study has the advantage of better intra-class compactness of clustering results.

Improved Fault Diagnosis Method Based on Probabilistic Neural Network

2012 ◽  
Vol 433-440 ◽  
pp. 6084-6088 ◽  
Author(s):  
Gu Qing Liu ◽  
Shu Hua Yin ◽  
Xin Tian Wang ◽  
Yan Qing Sun
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Probabilistic Neural Network ◽  
Digital Data ◽  
Improved Method ◽  
Attribute Weights ◽  
Novel Approach ◽  
Diagnosis Method ◽  
Fault Diagnosis System

In order to enhancing the accuracy of fault diagnosis system, an improved method based on the probabilistic neural network (PNN) is proposed, in which the synthetic attribute weights of faults are introduced that are obtained by integrating algebra view and information theory view of rough set. The synthetic attribute weights are utilized to training the classical PNN and dealing with the classification of faults so as to improving the PNN model. The new model is more accurate and can represent expertise. This novel approach is applied in digital data network to diagnose failures, and the results of the experiment verify that the method is practical and effective in raising accuracy of diagnosis as well as avoiding misdirection in fault remedy.

The Application of Local Mean Decomposition and Variable Predictive Model-Based Class Discriminate in Gear Fault Diagnosis

2014 ◽  
Vol 1014 ◽  
pp. 510-515 ◽  
Author(s):  
You Cai Xu ◽  
Xin Shi Li ◽  
Ran Tao ◽  
Shu Guo ◽  
Min Gou ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Predictive Model ◽  
Feature Vector ◽  
Local Mean Decomposition ◽  
Outer Race ◽  
Model Based ◽  
Gear Fault ◽  
Local Mean ◽  
Gear Fault Diagnosis ◽  
Non Stationary Signal

The time-domain energy message conveyed by vibration signals of different gear fault are different, so a method based on local mean decomposition (LMD) and variable predictive model-based class discriminate (VPMCD) is proposed to diagnose gear fault model. The vibration signal of gear which is the research object in this paper is decomposed into a series of product functions (PF) by LMD method. Then a further analysis is to select the PF components which contain main fault information of gear, the energy feature parameters of the selected PF components are used to form a fault feature vector. The variable predictive model-based class discriminate is a new multivariate classification approach for pattern recognition, through taking fully advantages of the fault feature vector. Finally, gear fault diagnosis is distinguished into normal state, inner race fault and outer race fault. The results show that LMD method can decompose a complex non-stationary signal into a number of PF components whose frequency is from high to low. And the method based on LMD and VPMCD has a high fault recognition function by analyzing the fault feature vector of PF.

Application of SVM Classifier and Fractal Feature in Circuit Fault Diagnosis

2012 ◽  
Vol 490-495 ◽  
pp. 942-945
Author(s):  
Jing Kui Mao ◽  
Xian Bai Mao
Keyword(s):  
Fault Diagnosis ◽  
Analog Circuits ◽  
Fractal Theory ◽  
Svm Classifier ◽  
Band Pass Filter ◽  
Pass Filter ◽  
Diagnosis Method ◽  
Diagnosis Accuracy ◽  
Band Pass ◽  
Circuit Fault Diagnosis

Combining SVM and fractal theory, a novel fault diagnosis method for analog circuits based on SVM using fractal dimension is developed in this paper. Simulation results of diagnosing the Sallen-Key band pass filter circuit have confirmed that the proposed approach increases the fault diagnosis accuracy, thereby it may be considered as an alternative for the analog fault diagnosis.

Fault Diagnosis of Large Rotors Based on Wavelet Packet Neural Network

2014 ◽  
Vol 722 ◽  
pp. 363-366
Author(s):  
You Juan Zheng ◽  
Ping Liao ◽  
Cai Long Qin ◽  
Yu Li
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Bp Neural Network ◽  
Test Bench ◽  
Feature Vector ◽  
Wavelet Packet ◽  
Vibration Signal ◽  
Network Method ◽  
Spectrum Characteristic ◽  
Fault Information

Using wavelet packet neural network method which is consist of wavelet packet and BP neural network to diagnose large rotors by vibration signal .Firstly , according to the spectrum characteristic of large rotors’ common vibration fault ,using the improved wavelet packet method to compute the energy of the spectrum that can reflect the fault information .And then make the feature vector as the input to establish a model of improved wavelet packet neural network for fault diagnosis . Collect the data of five working conditions from the test bench , establish a improved wavelet packet neural network model, and then use the model to diagnose fault. The experimental results show that this method improves the accuracy obviously and calculate fast.

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