scholarly journals Research on Fault Diagnosis Method Based on Rule Base Neural Network

2017 ◽  
Vol 2017 ◽  
pp. 1-7 ◽  
Author(s):  
Zheng Ni ◽  
Zhang Lin ◽  
Wang Wenfeng ◽  
Zhang Bo ◽  
Liu Yongjin ◽  
...  
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Fault Location ◽  
Fuzzy Theory ◽  
Learning Rule ◽  
Rule Base ◽  
Fuzzy Neural ◽  
Diagnosis Method ◽  
Construction Model ◽  
Network Fault

The relationship between fault phenomenon and fault cause is always nonlinear, which influences the accuracy of fault location. And neural network is effective in dealing with nonlinear problem. In order to improve the efficiency of uncertain fault diagnosis based on neural network, a neural network fault diagnosis method based on rule base is put forward. At first, the structure of BP neural network is built and the learning rule is given. Then, the rule base is built by fuzzy theory. An improved fuzzy neural construction model is designed, in which the calculated methods of node function and membership function are also given. Simulation results confirm the effectiveness of this method.

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.

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.

Fault Intelligent Diagnosis of Coal Mine Hoist Based on GCFNN

2013 ◽  
Vol 634-638 ◽  
pp. 3716-3720 ◽  
Author(s):  
Li Li Dong ◽  
Qing Qing Ding
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Coal Mine ◽  
Fuzzy Neural Network ◽  
Correct Diagnosis ◽  
Intelligent Diagnosis ◽  
Grey Clustering ◽  
Fuzzy Neural ◽  
Network Fault ◽  
Mine Hoist

Equipment running subtle condition can’t be clearly expressed by clustering result of explicit affiliation in the fuzzy neural network fault diagnosis. In order to solve the problems in the present, the integration of grey clustering theory and fuzzy neural network was researched, and the fault intelligent diagnosis methods based on grey clustering fuzzy neural network (GCFNN) was proposed, the structure and the algorithm of GCFNN were designed, and the model of GCFNN was established. In coal mine hoist hydraulic subsystem fault diagnosis as an example, the feasibility and validity of the method is simulated and verified. The experiment results show that GCFNN can make a correct diagnosis, express more detailed equipment condition information. The method proposed provides basis for the maintenance of the mine hoist, and provides a new approach for the fault diagnosis of the other mine equipment.

Power System Fault Diagnosis Method Summary

2014 ◽  
Vol 670-671 ◽  
pp. 1179-1183
Author(s):  
Yu Zhao ◽  
Wei Xiong ◽  
Huang Qiang Li ◽  
Shi Yong Yang
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Fault Diagnosis ◽  
Expert System ◽  
Power System ◽  
Bayesian Network ◽  
Current Situation ◽  
Advantages And Disadvantages ◽  
Diagnosis Method ◽  
Network Fault

Combined with the power system fault diagnosis current situation, fault diagnosis methods are important to shorten fault outage time, prevent accident expanding and restore power quickly. We summed up expert system, artificial neural network, Petri network and Bayesian network fault diagnosis methods. The diagnosis principle, advantages and disadvantages of different methods were discussed.

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