Fault diagnosis and testing of induction machine using Back Propagation Neural Network

2012 ◽  
Author(s):  
N. Rajeswaran ◽  
T. Madhu ◽  
M. Surya Kalavathi
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Back Propagation ◽  
Induction Machine ◽  
Back Propagation Neural Network

scholarly journals Chaotic Extension Neural Network-Based Fault Diagnosis Method for Solar Photovoltaic Systems

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Kuo-Nan Yu ◽  
Her-Terng Yau ◽  
Jian-Yu Li
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Photovoltaic System ◽  
Photovoltaic Systems ◽  
Solar Photovoltaic ◽  
Error Back Propagation ◽  
Point Tracking ◽  
Diagnosis Method

At present, the solar photovoltaic system is extensively used. However, once a fault occurs, it is inspected manually, which is not economical. In order to remedy the defect of unavailable fault diagnosis at any irradiance and temperature in the literature with chaos synchronization based intelligent fault diagnosis for photovoltaic systems proposed by Hsieh et al., this study proposed a chaotic extension fault diagnosis method combined with error back propagation neural network to overcome this problem. It used the nn toolbox of matlab 2010 for simulation and comparison, measured current irradiance and temperature, and used the maximum power point tracking (MPPT) for chaotic extraction of eigenvalue. The range of extension field was determined by neural network. Finally, the voltage eigenvalue obtained from current temperature and irradiance was used for the fault diagnosis. Comparing the diagnostic rates with the results by Hsieh et al., this scheme can obtain better diagnostic rates when the irradiances or the temperatures are changed.

Fault Diagnosis of Water Pump Based on Probabilistic Neural Network

2011 ◽  
Vol 338 ◽  
pp. 421-424
Author(s):  
Tie Jun Li ◽  
Yan Chun Zhao ◽  
Xin Li ◽  
Cheng Shi Zhu ◽  
Jian Rong Ning
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Basic Principle ◽  
Probabilistic Neural Network ◽  
Back Propagation ◽  
Vibration Signal ◽  
Back Propagation Neural Network ◽  
Signal Spectrum ◽  
Water Pump ◽  
Time Diagnosis

The basic principle of probabilistic neural network (PNN) is introduced, which is used in the fault diagnosis of water pump in this paper. The multiple and fractional frequencies in the fault vibration signal spectrum are taken as the feature vectors, and the samples of the fault are established. The probabilistic neural network is trained based on the symptom diagnosis. The result shows that probabilistic neural network can overcome the local optimization of back propagation neural network (BPNN) and meet the requirements for fast diagnosis and high precision diagnosis during fault diagnosis process, so probabilistic neural network can be used in the real time diagnosis, and the fault diagnosis based on probabilistic neural network is feasible.

scholarly journals BP Neural Network Algorithms for Fault Diagnosis of Microwave Components

2019 ◽  
Vol 95 ◽  
pp. 04008
Author(s):  
Gao Kun ◽  
Wang Aimin ◽  
Ge Yan
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Network Model ◽  
Neural Network Model ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Main Trend ◽  
Intelligent Fault Diagnosis ◽  
The Neural Network ◽  
Training Samples

Intelligent diagnosis is the main trend of modern fault diagnosis technology. The emergence of artificial neural network technology provides a new way for this kind of intellectualization. Aiming at the problem of microwave module fault diagnosis, an intelligent fault diagnosis method based on BP(Back Propagation) neural network is proposed in this paper. In this paper, the process of determining the neural network model and the operation flow of BP algorithm are introduced, and the network is trained with training samples. By applying the neural network model to an AQ module for testing, the feasibility, accuracy and efficiency of the fault diagnosis of the microwave module are verified, which provides a new method for intelligent fault diagnosis of this kind of microwave module.

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