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Based On Virtual Instrument Air Compressor Remote Monitoring And Fault Diagnosis

Posted on:2006-09-29Degree:MasterType:Thesis
Country:ChinaCandidate:X X LeiFull Text:PDF
GTID:2208360182968951Subject:Traffic Information Engineering & Control
Abstract/Summary:PDF Full Text Request
This article focuses on the realization methodology of the air compressor remote monitor and intelligent fault diagnosis system based on the virtual instrument. The system first obtain the data of a air compressor, these data include volt, electric current, pressure and temperature, then use these data to get the synthesis quality and the estimate of future synthesis quality through the intelligent fault diagnosis system. The main work was listed as follows:1. To utilize the close in upon any function capability of radial basis function to monitor and online diagnose the fault of the press sensor used in the air compressor. Studied its structure, learning method, the sensor's diagnose principle based on the radial basis function and the MATLAB simulation examples are given at the same time.2. To apply the method of the principle component analysis in the temperature sensor diagnose, analysis the calculation and data reconstruct means and the radial basis function is put forward on the research of the sensor precision decline.3. To meet the need of its trait and its practical production, the principal and subordinate multi-CPU embedded hardware design and the relevant software design was finished. Furthermore, the novel anti-jamming measure was adopted by combining of the hardware and software which were both restricted.4. Monitor program in master machine with friendly human-computer interface and perfect function is programmed by virtual instrument language— LabWindows/CVI.Intelligent fault diagnosis and remote monitor is a research field that has development potential, and research on the field has practicality value. The research of this article is a experiment work and the good result of this method used in practice proved the correctness of our research. Now, we are doing more research work in this field thoroughly and synthesized.
Keywords/Search Tags:Fault Diagnosis, Remote Monitoring, Artificial Neural network, Principle Component Analysis, Radial Basis Function
PDF Full Text Request
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