The Development of an Adaptive Electromagnetic Testing Method

This thesis focuses on the development of an adaptive method for conducting electromagnetic interference testing, which increases the efficiency of this kind of testing. The goal of this kind of testing is to find configurations that result in disturbances on the device under test. Often these tests have a very low rate of positive results, thus requiring a substantially large number of experiments to gleam meaningful conclusions. This problem is mitigated through the use of classification modeling techniques to inform the design of experiments and increase the rate of positive results. The first step in creating such as system was to develop a computer controlled data acquisition system in order to cut down on human error and make an efficient test cycle. This was done through the use of a Labview virtual instrument. The development of the this virtual instrument is presented and evaluated in the context of this investigation as well as future. After using the developed system to collect an adequate amount of data for building a classifier, an investigation into data processing and classification model building is presented. The industry use of logistic regression is explained and shown to be ineffective on this kind of data. Subsequently a look into the use of several kinds of parametric and non-parametric modeling techniques is presented, and the non-parametric methods found to be superior. Finally the use of a non-parametric K-nearest neighbor model to inform a design of experiments is presented and methods for adapting the model explained. This technique was shown to have a positive test result almost four times more often than traditional design of experiments techniques.

Name thesis:degree_name

M.S. (Master of Science)

Level thesis:degree_level

Masters

Discipline thesis:degree_discipline

Mechanical Engineering (UMKC)

Grantor

University of Missouri--Kansas City

Year dc:date.issued

2019

Author dc:creator

• Harp, Joshua Austin

Advisor dc:contributor.advisor

• Fields, Travis

Handle dc:identifier.uri

https://hdl.handle.net/10355/70904

OAI identifier oai:identifier

oai:mospace.umsystem.edu:10355/70904