Design of Auto-Detection Instrument for Airborne Thermometer System

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Abstract:

To improve the test precision of detection device for some airborne thermometer system, the hardware structure and software design of auto-detection instrument for airborne temperature indicator are introduced. The 89C52 single-chip computer is used in the detection instrument. The CPU、A/D、 D/A and I/O boards are assembled as a cascade structure. By using the technology of the advanced chip and Lab Windows / CVI 5.0, the instrument’s precision and flexibility is greatly enhanced. The testing results illustrate the testing resolution is 23Bit, the output electric current is 10mA, the precision is ±0.01mV.

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Periodical:

Advanced Materials Research (Volumes 468-471)

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875-878

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Online since:

February 2012

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© 2012 Trans Tech Publications Ltd. All Rights Reserved

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[1] Joseph Giarratano, Gary Riley. Expert Systems Principles and Programming [M]. Machinery Industry Publishing Company,2000。

Google Scholar

[2] Yao Zhi-gang, Fu Qiang, Wu Bo-mao, Tan Xiao-chen, Design of Testing Platform for Fault Diagnosis of one New Equipment [J]. Measurement and Control Technology, 2006,25(4):30~32.

Google Scholar

[3] Alessandro S, Antonina S. Speed up learning and network optimization with extended back propagation [J].Neural Networks, 1993, 6:365~383.

DOI: 10.1016/0893-6080(93)90004-g

Google Scholar

[4] Liu Rong, Zhou Ming-qi. Fault and diagnosis expert system summarization [J]. Measurement and Control Technology,1994,13(3):6~9.

Google Scholar

[5] Ishibuchi H. Implementation of fuzzy if-then rules by fuzzy neural networks with fuzzy weight [A].Proc of 1st European conger. [G]. On fuzzy and intelligent technologies, Germany, 1993. 173~178.

Google Scholar

[6] Wu Ming-kiang, Shi Hue, Zhu Xiao-hua, Xiao Kai-qing. Research and Prospect of Fault Diagnosis Expert System [J]. Computer Measurement and Control, 2005,13(12):1301~1304.

Google Scholar

[7] Ishibuchi H, Nii M. Numerical analysis of the learning of fuzzified neural networks forms fuzzy if-then rules [J]. Fuzzy sets and Systems, 2001(120):281-307.

DOI: 10.1016/s0165-0114(99)00070-6

Google Scholar

[8] Seong C, Widrom B. Neural dynamic optimization for control systems-PartⅡ, theory[J].IEEE Trans on Systems, Man, and Cybernetics, 2001,31(4):490~501.G. Eason, B. Noble, and I. N. Sneddon, "On certain integrals of Lipschitz-Hankel type involving products of Bessel functions," Phil. Trans. Roy. Soc. London, vol. A247, p.529–551, April 1955.

DOI: 10.1098/rsta.1955.0005

Google Scholar