Validation of Worst-Case and Statistical Models for an Automotive EMC Expert System

Previous papers have presented algorithms for an EMC expert system used to predict potential electromagnetic compatibility problems in a vehicle early in the design process. Here, the accuracy of inductive and capacitive coupling algorithms are verified through representative measurements of crossta...

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Main Authors: Beetner, D.G., Haixiao Weng, Meilin Wu, Hubing, T.
Format: Conference Proceeding
Language:English
Subjects:
Algorithm design and analysis
Automobiles
Automotive engineering
Crosstalk
Electromagnetic compatibility
Electromagnetic measurements
Expert systems
Process design
Statistical analysis
Vehicles
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Haixiao Weng
Meilin Wu
Hubing, T.
description Previous papers have presented algorithms for an EMC expert system used to predict potential electromagnetic compatibility problems in a vehicle early in the design process. Here, the accuracy of inductive and capacitive coupling algorithms are verified through representative measurements of crosstalk within an automobile. Worst-case estimates used by the algorithms are compared to measured values and are compared to values estimated using statistical methods. The worst-case algorithms performed well up to 10-20 MHz, but overestimated measured results by several dB in some cases and up to 10-15 dB in others. An approximate statistical variation of the current expert system algorithms also worked well and can help avoid overestimation of problems; however, worst-case estimates better ensure that problems will not be missed, especially in the absence of complete system information.
doi_str_mv 10.1109/ISEMC.2007.34
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subjects Algorithm design and analysis
Automobiles
Automotive engineering
Crosstalk
Electromagnetic compatibility
Electromagnetic measurements
Expert systems
Process design
Statistical analysis
Vehicles
title Validation of Worst-Case and Statistical Models for an Automotive EMC Expert System
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