Multi-Resolution WNN Fault Diagnosis Model Based on Unscented Kalman Filtering for Rotating Machinery

2014 ◽  
Vol 687-691 ◽  
pp. 1030-1033
Author(s):  
Yan Ming Wei ◽  
Xu Sheng Gan ◽  
Jie Yang
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Bp Neural Network ◽  
Rotating Machinery ◽  
Wavelet Neural Network ◽  
Local Minima ◽  
Learning Rules ◽  
Fast Training ◽  
The Relationship ◽  
Better Than

BP neural network has a good nonlinear mapping ability, and can describe the relationship between frequency characteristics and fault. However, the multi-resolution wavelet neural network has the simple learning rules, fast training speed with the avoidance of local minima. So a multi-resolution wavelet neural network based on UKF is proposed to solve the problem of fault diagnosis for rotating machinery. The simulation result shows that the proposed multi-resolution wavelet neural network based on UKF value has a good diagnosis capability, and is better than that of traditional BP neural network and wavelet neural network.

Fault Diagnosis of Rolling Bearing on the Basis of Wavelet Neural Network

2014 ◽  
Vol 598 ◽  
pp. 244-249
Author(s):  
Song Lin Wu ◽  
Jian Xin Liu ◽  
Li Li
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Fault Diagnosis ◽  
Wavelet Analysis ◽  
Bp Neural Network ◽  
Basis Function ◽  
Feature Vector ◽  
Roller Bearing ◽  
Rolling Bearing ◽  
Wavelet Neural Network

In this paper, the feature vector of the roller bearing signals are extracted on the basis of wavelet analysis and a fault diagnosis experiment is carried through wavelet neural network in detail. The method and the theory of fault diagnosis based on BP neural network and the radial basis function neural network are studied and the results of diagnosis based on relax-type Neural-Networks and close-type Neural-Networks are compared.

Fault Diagnosis of Tolerance Analog Circuit Based on Wavelet Neural Network with PSO Algorithm

2013 ◽  
Vol 307 ◽  
pp. 312-315 ◽  
Author(s):  
Wei Cong ◽  
Bo Jing ◽  
Hong Kun Yu
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Analog Circuit ◽  
Pso Algorithm ◽  
Wavelet Neural Network ◽  
Parameters Optimization ◽  
Bp Algorithm ◽  
Algorithm Performance ◽  
Fast Training ◽  
Diagnosis Method

For the Difficulties in fault diagnosis of tolerance analog circuit, a Wavelet Neural Network (WNN) diagnosis method based on Particle Swarm Optimization (PSO) algorithm is proposed. To overcome the deficiencies of the traditional BP algorithm using in WNN, PSO algorithm is introduced into the parameters optimization in WNN, and the velocity disturbance operator is embedded to ensure the particle out of the premature position for PSO algorithm performance. The simulation results show that the proposed method has the fast training rate, accurate diagnosis, without local convergence.

scholarly journals Fault diagnosis of wind turbine gearbox based on wavelet neural network

2018 ◽  
Vol 37 (4) ◽  
pp. 977-986 ◽  
Author(s):  
Chen Huitao ◽  
Jing Shuangxi ◽  
Wang Xianhui ◽  
Wang Zhiyang
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Wind Turbine ◽  
Network Model ◽  
Vibration Signal ◽  
Wavelet Neural Network ◽  
Wind Turbine Gearbox ◽  
Diagnosis Method ◽  
Fault Mechanism ◽  
The Relationship

In order to monitor the wind turbine gearbox running state effectively, a fault diagnosis method of wind turbine gearbox is put forward based on wavelet neural network. Taking a 1.5 MW wind turbine gearbox as the target of study, the frequency spectrum of vibration signal and the fault mechanism of driving part are analyzed, and the eigenvalues of the frequency domain are extracted. A wavelet neural network model for fault diagnosis of wind turbine gearbox is established, and wavelet neural network is trained by using different feature vectors of fault types. The relationship between fault component and vibration signal is identified, and the vibration fault of wind turbine gearbox is predicted and diagnosed by network model. The analysis results show that the method can diagnose fault and fault pattern recognition of wind turbine gearbox very well.

Wavelet Neural Network Based on EKF Algorithm and its Application in Fault Diagnosis for Rotating Machinery

2014 ◽  
Vol 602-605 ◽  
pp. 1741-1744
Author(s):  
Xu Sheng Gan ◽  
Hua Ping Li ◽  
Hai Long Gao
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Rotating Machinery ◽  
Wavelet Neural Network ◽  
Model Based ◽  
Proposed Model ◽  
Reliable Diagnosis ◽  
Diagnosis Model

It is difficult to realize an accurate and reliable diagnosis in the rotating machinery. To solve this problem, a Wavelet Neural Network (WNN) diagnosis model based on EKF algorithm is proposed. In the model, EKF algorithm is introduced to optimize the parameters of WNN, and then the built WNN model is used to diagnose the faults of the rotating machinery. The experiment shows that, the proposed model has a good diagnosis capability in the field of the rotating machinery.

Improved PSO-BP neural network for power transformer fault diagnosis

2010 ◽  
Vol 30 (3) ◽  
pp. 783-785 ◽  
Author(s):  
Zhong-yang XIONG ◽  
Qing-bo YANG ◽  
Yu-fang ZHANG
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Bp Neural Network ◽  
Power Transformer ◽  
Transformer Fault ◽  
Improved Pso

PNN and BP Neural Network Fault Diagnosis Research on Electronic Accelerator Pedal Detection

2020 ◽  
Vol 14 (2) ◽  
pp. 205-220
Author(s):  
Yuxiu Jiang ◽  
Xiaohuan Zhao
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Fault Detection ◽  
Lower Limit ◽  
Bp Neural Network ◽  
Accelerator Pedal ◽  
Electronic Throttle ◽  
Measured Voltage ◽  
Upper Limit ◽  
Network Fault

Background: The working state of electronic accelerator pedal directly affects the safety of vehicles and drivers. Effective fault detection and judgment for the working state of the accelerator pedal can prevent accidents. Methods: Aiming at different working conditions of electronic accelerator pedal, this paper used PNN and BP diagnosis model to detect the state of electronic accelerator pedal according to the principle and characteristics of PNN and BP neural network. The fault diagnosis test experiment of electronic accelerator pedal was carried out to get the data acquisition. Results: After the patents for electronic accelerator pedals are queried and used, the first measured voltage, the upper limit of first voltage, the first voltage lower limit, the second measured voltage, the upper limit of second voltage and the second voltage lower limit are tested to build up the data samples. Then the PNN and BP fault diagnosis models of electronic accelerator pedal are established. Six fault samples are defined through the design of electronic accelerator pedal fault classifier and the fault diagnosis processes are executed to test. Conclusion: The fault diagnosis results were analyzed and the comparisons between the PNN and the BP research results show that BP neural network is an effective method for fault detection of electronic throttle pedal, which is obviously superior to PNN neural network based on the experiment data.

Research of Fault Diagnosis of Belt Conveyor Based on Fuzzy Neural Network

2014 ◽  
Vol 8 (1) ◽  
pp. 916-921
Author(s):  
Yuan Yuan ◽  
Wenjun Meng ◽  
Xiaoxia Sun
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Bp Neural Network ◽  
Fault Location ◽  
Fuzzy Neural Network ◽  
Fuzzy Theory ◽  
Belt Conveyor ◽  
Fuzzy Neural ◽  
Neural Network Algorithm ◽  
Network Fault

To address deficiencies in the process of fault diagnosis of belt conveyor, this study uses a BP neural network algorithm combined with fuzzy theory to provide an intelligent fault diagnosis method for belt conveyor and to establish a BP neural network fault diagnosis model with a predictive function. Matlab is used to simulate the fuzzy BP neural network fault diagnosis of the belt conveyor. Results show that the fuzzy neural network can filter out unnecessary information; save time and space; and improve the fault diagnosis recognition, classification, and fault location capabilities of belt conveyor. The proposed model has high practical value for engineering.

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