Application of a Optimized Wavelet Neural Networks in Rolling Bearing Fault Diagnosis

2012 ◽  
Vol 190-191 ◽  
pp. 919-922 ◽  
Author(s):  
Yuan Yan Lin ◽  
Bin Wu Wang
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Particle Swarm Optimization ◽  
Fault Diagnosis ◽  
Rolling Bearing ◽  
Wavelet Neural Networks ◽  
Rolling Bearings ◽  
Swarm Optimization ◽  
Bearing Fault Diagnosis ◽  
Fault Type

According to the fault type and fault signal of rolling bearing is difficult to predict, the paper proposed a new method to diagnose fault of rolling bearings with the wavelet neural network optimizated by simulated annealing particle swarm optimization. And it was applied to the fault diagnosis of rolling bearing. The experiment shows that this method can reduce the iteration time and improve the accuracy of convergence.

Research on Application of Blind Source Separation in Rolling Bearing Fault Diagnosis Based on Particle Swarm Optimization

2014 ◽  
Vol 971-973 ◽  
pp. 1321-1324
Author(s):  
Hao Zhou ◽  
Chang Zheng Chen ◽  
Xian Ming Sun ◽  
Huan Liu
Keyword(s):  
Signal Processing ◽  
Particle Swarm Optimization ◽  
Fault Diagnosis ◽  
Blind Source Separation ◽  
Source Separation ◽  
Particle Swarm ◽  
Rolling Bearing ◽  
Swarm Optimization ◽  
Bearing Fault Diagnosis ◽  
Research On Application

This paper presents blind source separation of rolling bearing based on particle swarm optimization. The algorithm combines the advantages of both blind source separation and particle swarm optimization. Through the experiment it is shown that the algorithm can separate the signals collected from rolling bearing and gearbox effectively, which can provide a new method for fault diagnosis and signal processing of machinery equipment.

Rolling Bearing Fault Diagnosis Method Using Glowworm Swarm Optimization and Artificial Neural Network

2013 ◽  
Vol 860-863 ◽  
pp. 1812-1815 ◽  
Author(s):  
Qiang Xu ◽  
Yong Qian Liu ◽  
De Tian ◽  
Quan Long
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Bp Neural Network ◽  
Rolling Bearing ◽  
Maintenance Cost ◽  
Swarm Optimization ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Glowworm Swarm Optimization ◽  
Potential Applications

Fault diagnosis has long been recognised as one of the most effective methods of reducing operation and maintenance cost in rotating industry, especially in bearings. A method based on BP neural network modified by glowworm swarm optimization (GSO) was proposed for fault diagnosis of rolling bearings. Six fault features were selected as the input of network. GSO algorithm was applied to simultaneously optimize the initial weight and threshold values of BP neural network. The reliability of the proposed technique was confirmed by experimental data, which indicated the potential applications of this method in the field of rolling bearing fault diagnosis.

Quantitative Analysis for SF6 and its Compositions in GIS

2012 ◽  
Vol 562-564 ◽  
pp. 1336-1339
Author(s):  
Hai Lun Wang ◽  
Jian Wei Shen
Keyword(s):  
Neural Network ◽  
Particle Swarm Optimization ◽  
Quantitative Analysis ◽  
Fault Diagnosis ◽  
Traditional Method ◽  
Particle Swarm ◽  
Prediction Errors ◽  
Swarm Optimization ◽  
Training Analysis ◽  
Derivatives Of

In this paper, a method for GIS equipment fault diagnosis by the analysis of volume fractions of the derivatives of SF6 gas inside GIS equipment is presented. For the method, based on the differential spectra method, a neural network model and the particle swarm optimization are used for training analysis of infrared spectra, to realize the quantitative analysis of specific derivatives. The experimental results show that the prediction errors obtained by particle swarm optimization training are markedly superior to prediction errors obtained using the traditional method.

Power Transform Fault Diagnosis Based on the Adaptive Mutation Particle Swarm Optimization

2013 ◽  
Vol 756-759 ◽  
pp. 3804-3808
Author(s):  
Zhi Mei Duan ◽  
Jia Tang Cheng
Keyword(s):  
Neural Network ◽  
Particle Swarm Optimization ◽  
Fault Diagnosis ◽  
Power Transformer ◽  
Recognition Rate ◽  
Particle Swarm ◽  
Adaptive Mutation ◽  
Bp Network ◽  
Swarm Optimization ◽  
Fault Recognition

In order to improve the accuracy of fault diagnosis of power transformer, in this paper, a method is proposed that optimize the weight of BP neural network by adaptive mutation particle swarm optimization (AMPSO). According to the characteristic of transformer fault, the optimized neural network is used to diagnose fault of the power transformer. Individual particles action is amended by this algorithm and local minima problems of the standard PSO and BP network are overcooked. The experimental results show that, the method can classify transformer faults, and effectively improve the fault recognition rate.

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