Diesel fault diagnosis technology based on the theory of fuzzy neural network information fusion

2003 ◽  
Author(s):  
Li Hongkun Ma ◽  
Xiaojiang He Yong
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Information Fusion ◽  
Fuzzy Neural Network ◽  
Network Information ◽  
Fuzzy Neural

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.

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.

scholarly journals Research on Fault Diagnosis of Hydraulic System of Fast Erecting Device Based on Fuzzy Neural Network

2022 ◽  
Author(s):  
Yangbing Zheng ◽  
Xiao Xue ◽  
Jisong Zhang
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Bp Neural Network ◽  
Hydraulic System ◽  
Fuzzy Neural Network ◽  
Simulation Analysis ◽  
Fuzzy Relation ◽  
Analysis Procedure ◽  
Fuzzy Neural ◽  
Simulation Results

In order to improve the fault diagnosis effectiveness of hydraulic system in erecting devices, the fuzzy neural neural network is applied to carry out fault diagnosis of hydraulic system. Firstly, the main faults of hydraulic system of erecting mechanism are summarized. The main faults of hydraulic system of erecting devices concludes abnormal noise, high temperature of hydraulic oil of hydraulic system, leakage of hydraulic system, low operating speed of hydraulic system, and the characteristics of different faults are analyzed. Secondly, basic theory of fuzzy neural network is studied, and the framework of fuzzy neural network is designed. The inputting layer, fuzzy layer, fuzzy relation layer, relationship layer after fuzzy operation and outputting layer of fuzzy neural network are designed, and the corresponding mathematical models are confirmed. The analysis procedure of fuzzy neural network is established. Thirdly, simulation analysis is carried out for a hydraulic system in erecting device, the BP neural network reaches convergence after 600 times iterations, and the fuzzy neural network reaches convergence after 400 times iterations, fuzzy neural network can obtain higher accuracy than BP neural network, and running time of fuzzy neural network is less than that of BP neural network, therefore, simulation results show that the fuzzy neural network can effectively improve the fault diagnosis efficiency and precision. Therefore, the fuzzy neural network is reliable for fault diagnosis of hydraulic system in erecting devices, which has higher fault diagnosis effect, which can provide the theory basis for healthy detection of hydraulic system in erecting devices.

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