Fault Detection and Diagnosis for Nonlinear System Based on Neural Network on-line Approximator

2006 ◽  
Author(s):  
Li Guo ◽  
Yantao Tian ◽  
Ming Fang
Keyword(s):  
Neural Network ◽  
Nonlinear System ◽  
Fault Detection ◽  
Fault Detection And Diagnosis ◽  
On Line ◽  
Detection And Diagnosis

A Method of Fault Detection and Diagnosis for Electromagnetic Direction Valve Based on Artificial Neural Network

2012 ◽  
Vol 476-478 ◽  
pp. 2384-2388
Author(s):  
Min Qiang Dai ◽  
Wei Cai ◽  
Sheng Dun Zhao
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Fault Detection ◽  
Wavelet Packet ◽  
Vibration Signal ◽  
Fault Detection And Diagnosis ◽  
Line Detection ◽  
On Line ◽  
Artificial Neural ◽  
Detection And Diagnosis

The magnetic field and vibration signal of electromagnetic direction valve can be detected real-timely by a non-intrusive on line detection device, which can use to monitor working state of the valve. A method of fault detection and diagnosis for electromagnetic direction valve from the signal detected by the non-intrusive on line detection device is presented in this paper. The wave frequency bands energy analysis method is adopted to distinguish the electromagnetic direction valve’s state, and the vibration signal are decomposed by three-layer wavelet packet which wavelet basis is db10. The fault identification method is based on BP artificial neural network (ANN), which is the most well-known three-layers BP ANN whose input and output layers have 8 and 3 neurons respectively.

Detection and Diagnosis of Broken Rotor Bars in Induction Motors Using the Fuzzy Min-Max Neural Network

2012 ◽  
Vol 3 (1) ◽  
pp. 44-55 ◽  
Author(s):  
Manjeevan Seera ◽  
Chee Peng Lim ◽  
Dahaman Ishak
Keyword(s):  
Neural Network ◽  
Fault Detection ◽  
Induction Motor ◽  
Induction Motors ◽  
Fault Detection And Diagnosis ◽  
Broken Rotor Bars ◽  
Power Spectral ◽  
Motor Current Signature Analysis ◽  
Detection And Diagnosis ◽  
Current Signatures

In this paper, a fault detection and diagnosis system for induction motors using motor current signature analysis and the Fuzzy Min-Max (FMM) neural network is described. The finite element method is first employed to generate experimental data for predicting the changes in stator current signatures of an induction motor due to broken rotor bars. Then, a series real laboratory experiments is for broken rotor bars detection and diagnosis. The induction motor with broken rotor bars is operated under different load conditions. In all the experiments, the FMM network is used to learn and distinguish between normal and faulty states of the induction motor based on the input features extracted from the power spectral density. The experimental results positively demonstrate that the FMM network is useful for fault detection and diagnosis of broken rotor bars in induction motors.

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