Based on Information Fusion Integrated Wavelet Neural Network Fault Diagnosis

2011 ◽  
Vol 219-220 ◽  
pp. 1077-1080
Author(s):  
Dong Yan Cui ◽  
Zai Xing Xie
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Fault Diagnosis ◽  
Information Fusion ◽  
Wavelet Neural Network ◽  
Wavelet Neural Networks ◽  
Effective Combination ◽  
Fault Diagnosis System ◽  
Network Fault ◽  
Fault Characteristic

In this paper, the integration of wavelet neural network fault diagnosis system is established based on information fusion technology. the effective combination of fault characteristic information proves that integration of wavelet neural networks make better use of a variety of characteristic information than the list of wavelet neural networks to solve difficulties and problems which are difficult to resolve by a single network.

Application of Integrated Neural Network Information Fusion in Fault Diagnosis of one Missile Launching Control Unit

2013 ◽  
Vol 427-429 ◽  
pp. 2808-2812
Author(s):  
Xu De Cheng ◽  
Hong Li Wang ◽  
Bing Xu ◽  
Xue Dong Xue
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Information Fusion ◽  
Preliminary Analysis ◽  
Control Unit ◽  
Fusion Technology ◽  
Network Information ◽  
Diagnosis Rate ◽  
Fault Diagnosis System ◽  
Made In

Research and development of fault diagnosis system in application of integrated neural network information fusion is based on information fusion technology, with which preliminary analysis of equipment fault is made in different perspectives in terms of neural network, so as to identify the fault on the basis of fusion outcome. This technique is applied in fault diagnosis of one type of missile launching control unit, leading to sufficient use of various information and substantially increased fault diagnosis rate.

Modeling of coal consumption rate based on wavelet neural network

2017 ◽  
Vol 08 (03) ◽  
pp. 1750023
Author(s):  
Xiaoqiang Wen ◽  
Shuguang Jian
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Consumption Rate ◽  
Orthogonal Experiment ◽  
Thermal Power ◽  
Morlet Wavelet ◽  
Wavelet Neural Network ◽  
Wavelet Function ◽  
Wavelet Neural Networks ◽  
Coal Consumption

In this paper, two wavelet neural network (WNN) frames which depend on Morlet wavelet function and Gaussian wavelet function were established. In order to improve the efficiency of model training, the momentum term was applied to modify the weights and thresholds, and the output of the network was summed up by function transformation of output layer nodes. When the Gaussian Wavelet Neural Networks (GWNN) and Morlet Wavelet Neural Networks (MWNN) were applied to coal consumption rate (CCR) estimation in a thermal power plant, the results confirmed their potency in function approximation. In addition, the influence of learning rate on the models was also discussed through the orthogonal experiment.

scholarly journals Wind Speed Forecasting by Wavelet Neural Networks: A Comparative Study

2013 ◽  
Vol 2013 ◽  
pp. 1-7 ◽  
Author(s):  
Chuanan Yao ◽  
Xiankun Gao ◽  
Yongchang Yu
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Wind Speed ◽  
Vital Role ◽  
Wavelet Neural Network ◽  
System Stability ◽  
Wavelet Neural Networks ◽  
Grid Security ◽  
The Third ◽  
Combined Models

Due to the environmental degradation and depletion of conventional energy, much attention has been devoted to wind energy in many countries. The intermittent nature of wind power has had a great impact on power grid security. Accurate forecasting of wind speed plays a vital role in power system stability. This paper presents a comparison of three wavelet neural networks for short-term forecasting of wind speed. The first two combined models are two types of basic combinations of wavelet transform and neural network, namely, compact wavelet neural network (CWNN) and loose wavelet neural network (LWNN) in this study, and the third model is a new hybrid method based on the CWNN and LWNN models. The efficiency of the combined models has been evaluated by using actual wind speed from two test stations in North China. The results show that the forecasting performances of the CWNN and LWNN models are unstable and are affected by the test stations selected; the third model is far more accurate than the other forecasting models in spite of the drawback of lower computational efficiency.

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.

scholarly journals Multidimensional wavelet neural networks Based on polynomial powers of sigmoid

DAT Journal ◽  
2016 ◽  
Vol 1 (2) ◽  
pp. 106-123
Author(s):  
João Fernando Marar ◽  
Aron Bordin
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Back Propagation ◽  
Activation Function ◽  
Wavelet Neural Network ◽  
Back Propagation Algorithm ◽  
Wavelet Neural Networks ◽  
Low Dimensionality ◽  
Feed Forward Neural Networks ◽  
Sigmoid Functions

Wavelet functions have been used as the activation function in feed forward neural networks. An abundance of R&D has been produced on wavelet neural network area. Some successful algorithms and applications in wavelet neural network have been developed and reported in the literature. However, most of the aforementioned reports impose many restrictions in the classical back propagation algorithm, such as low dimensionality, tensor product of wavelets, parameters initialization, and, in general, the output is one dimensional, etc. In order to remove some of these restrictions, a family of polynomial wavelets generated from powers of sigmoid functions is presented. We described how a multidimensional wavelet neural networks based on these functions can be constructed, trained and applied in pattern recognition tasks. As examples of applications for the method proposed a framework for face verfication is presented.

Study of the Fault Diagnosis Method Based on Wavelet Time and Frequency Analysis

2012 ◽  
Vol 472-475 ◽  
pp. 2166-2170
Author(s):  
Qun Qi ◽  
Xue Zhang Zhao
Keyword(s):  
Neural Network ◽  
Wavelet Transform ◽  
Fault Diagnosis ◽  
Asynchronous Motor ◽  
Wavelet Neural Network ◽  
Experiment Data ◽  
Complex Fault ◽  
Diagnosis Method ◽  
Neural Network Algorithm ◽  
Network Fault

In order to better solve asynchronous motor complex fault characteristics, improve the reliability of the diagnosis and accuracy, combined with wavelet transform technique, construct a wavelet neural network, wavelet transform technology feature extraction asynchronous motor as a signal wavelet neural network's input vector, and the wavelet neural network algorithm was used to optimize, realize the motor identify types of fault, through the simulation experiment data diagnosis results show that this method is effective and feasible. Based on the wavelet analysis and neural network fault diagnosis method of research.

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