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Remote fault diagnosis technology based on self-organizing neural network

Author: LiuJingBo
Tutor: ChenWenYu
School: University of Electronic Science and Technology
Course: Software Engineering
Keywords: Neural Network Fault Diagnosis Synchronous growth of the region Vector field Analytic Hierarchy Process
CLC: TP277
Type: Master's thesis
Year: 2008
Downloads: 189
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Abstract


Remote fault diagnosis technology plays an important role in the operation and management of machinery and equipment and breakdown maintenance support. Can immediately find the machinery and equipment failures and deal with it effectively, improve the efficiency of machinery and equipment; reduce maintenance costs, to avoid \With the machinery and equipment increasingly large and complex automation, intelligent mechatronics, diagnostic techniques Forward intelligent direction. Neural network as the representative of Computational Intelligence technology provides an effective way for remote intelligent fault diagnosis. This paper in order to improve the fault pattern recognition accuracy was the purpose of the main line to the fault diagnosis of data processing, based on the parallel SOM neural network, fault tolerance and self-organization learning ability, the use of integrated information technology, remote fault diagnosis technology studied. The main contents are as follows: 1. Fault diagnosis based data preprocessing multilayer SOM remote fault diagnosis. Failure to improve the fault information data using the average of the noise reduction processing; comparative analysis of the PCA, ICA carried out the effect of reducing the dimension of fault data and combined with the advantages for data dimensionality reduction processing; multilayer the SOM make the pattern clustering area convergence Pattern recognition rate. Growth failure pattern recognition based on SOM feature map synchronization area. SOM neural network cluster analysis, the input data generated on SOM feature map seed region, synchronous growth completion of various sub-regional pattern clustering division of the region and the failure pattern recognition. 3.SOM feature map on the vector field failure pattern recognition method. SOM neural network cluster analysis, the input data generated in the SOM feature map seed region, the seed region of the gravitational field on the the gravitational source structure SOM feature map fault, according to the the feature map points along the gravitational movement convergence pattern recognition. Analytic hierarchy process fault diagnosis information fusion. According to the basic principles of information fusion, the calculation of a variety of typical failure mode input failure mode to measure and compare in pairs, using the analytic hierarchy process to integrate the different neural network fault diagnosis subsystem recognition results to improve the rate of correct identification of failure modes. In the above study, the use of matlab tool for fault diagnosis simulation of an aircraft landing gear failure, and achieved good results.

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CLC: > Industrial Technology > Automation technology,computer technology > Automation technology and equipment > Automation systems > Monitoring, alarm,fault diagnosis system
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