Fault Monitoring and Diagnosis of Induction Machines Based on Harmonic Wavelet Transform and Wavelet Neural Network

2008 ◽  
Author(s):  
Qianjin Guo ◽  
Xiaoli Li ◽  
Haibin Yu ◽  
Xiangzhi Che ◽  
Wei Hu ◽  
...  
Keyword(s):  
Neural Network ◽  
Wavelet Transform ◽  
Induction Machines ◽  
Wavelet Neural Network ◽  
Monitoring And Diagnosis ◽  
Fault Monitoring ◽  
Harmonic Wavelet

Study on the Onset of Nucleate Boiling in Narrow Annular Channel by Using Wavelet and Wavelet Neural Network

2009 ◽  
Author(s):  
Huiming Wei ◽  
G. H. Su ◽  
S. Z. Qiu ◽  
Xingbo Yang
Keyword(s):  
Neural Network ◽  
Wavelet Transform ◽  
Annular Channel ◽  
Nucleate Boiling ◽  
Wavelet Neural Network ◽  
Frequency Space ◽  
Time Frequency ◽  
Annular Channels ◽  
System Pressure ◽  
Artificial Neural

In this study, the local modulus maxima of cubic B-spline wavelet transform are introduced to determine the location of onset of nucleate boiling (ONB). Wavelet transformation has the ability of representing a function and revealing the properties of the function in the joint local regions of the time frequency space. Based on wavelet and artificial neural network, a Wavelet Neural Network (WNN) model predicting ONB for upward flow in vertical narrow annuli with bilateral heating has been developed. The WNN mode combining the properties of the wavelet transform and the advantages of Artificial Neural Networks (ANN) has some advantages of solving non-linear problem. The methods of establishing the model and training of wavelet neural network are discussed particularly in the article. The ONB prediction is investigated by WNN with distilled water flowing upward through narrow annular channels with 0.95 mm, 1.5 mm and 2.0mm gaps, respectively. The WNN prediction results have a good agreement with experimental data. At last, the main parametric trends of the ONB are analyzed by applying WNN. The influences of system pressure, mass flow velocity and wall superheat on ONB are obtained. Simulation and analysis results show that the network model can effectually predict ONB.

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.

scholarly journals A wavelet neural control scheme for a quadrotor unmanned aerial vehicle

2018 ◽  
Vol 376 (2126) ◽  
pp. 20170248 ◽  
Author(s):  
F. Jurado ◽  
S. Lopez
Keyword(s):  
Neural Network ◽  
Wavelet Transform ◽  
Unmanned Aerial Vehicle ◽  
Continuous Wavelet Transform ◽  
Morlet Wavelet ◽  
Wavelet Neural Network ◽  
Continuous Wavelet ◽  
Continuous Wavelets ◽  
Control Scheme ◽  
Aerial Vehicle

Wavelets are designed to have compact support in both time and frequency, giving them the ability to represent a signal in the two-dimensional time–frequency plane. The Gaussian, the Mexican hat and the Morlet wavelets are crude wavelets that can be used only in continuous decomposition. The Morlet wavelet is complex-valued and suitable for feature extraction using the continuous wavelet transform. Continuous wavelets are favoured when high temporal and spectral resolution is required at all scales. In this paper, considering the properties from the Morlet wavelet and based on the structure of a recurrent high-order neural network model, a novel wavelet neural network structure, here called a recurrent Morlet wavelet neural network, is proposed in order to achieve a better identification of the behaviour of dynamic systems. The effectiveness of our proposal is explored through the design of a decentralized neural backstepping control scheme for a quadrotor unmanned aerial vehicle. The performance of the overall neural identification and control scheme is verified via simulation and real-time results. This article is part of the theme issue ‘Redundancy rules: the continuous wavelet transform comes of age’.

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