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Research on Remote Fault Diagnosis System of NC Cutting Machine

Author: JiangHaiJun
Tutor: YangHuiXian
School: Xiangtan University
Course: Control Theory and Control Engineering
Keywords: CNC cutting plotter Fault Diagnosis Fuzzy Neural Network BP algorithm
CLC: TG659
Type: Master's thesis
Year: 2008
Downloads: 37
Quote: 1
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Abstract


With the continuous improvement of the degree of automation, light manufacturing , CNC cutting plotter application promotion, especially in clothing, footwear, luggage , leather , glass and carved industry , the application of the CNC cutting plotter greatly improve the production efficiency . But when the cut plotter failure , general technical staff can not be accurately analyzed to determine the cause of the malfunction , factory professional and technical personnel on-site service , it will affect the production , CNC cutting plotter fault diagnosis system therefore has great practical value . This paper first analyzes the current development of domestic and foreign remote fault diagnosis technology , describes the various diagnostic methods available . According to the characteristics of remote fault diagnosis system , the establishment of a the diagnostic network framework model , and detailed analysis and comparison of the C / S ( client / server ) model and the B / S ( browser / server ) model to establish a network model . Then introduces the theory of fuzzy control and neural network , fuzzy control theory and neural networks have their own strengths , they combine to form the fuzzy neural network ( Fuzzy Neural Network -FNN ) . Based the FNN fault diagnosis technology will be fully embodies the advantages of neural networks and fuzzy control theory is a very promising research direction , and its applications are increasingly being used . Finally, the structural characteristics and working principle of CNC cutting plotter , CNC cutting plotter of some of the common faults and workarounds . The fuzzy processing based on the the cutting plotter failure characteristics will reflect the characteristics of the fault signal to make it better reflect the failure of randomness and uncertainty , using a neural network learning algorithm to acquire knowledge . Improved BP learning algorithm used in the error backpropagation not only be trimmed to network weights also amended the compensation parameters in the fuzzy operator . Fewer opportunities for the local minimum points , more accurate , more network mapping function approximation . Simulation results prove the feasibility of the fuzzy neural network fault diagnosis .

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CLC: > Industrial Technology > Metallurgy and Metal Craft > Metal cutting and machine tools > Program control machine tools, CNC machine tools and machining
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