Power Quality Test Data Analysis for Aircraft Subsystem

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Authors Abstract
Content
Aircraft subsystem development involves various combinations of testing and qualification activities to realize a flight-worthy system. The subsystem needs to be verified for a massive number of customer requirements. Power quality (PQ) test is also an important testing activity carried out as part of the environmental qualification test. It is intended to verify the functionality of subsystems with various kinds of power disturbances and to determine the ability of a subsystem to withstand PQ disturbances. The subsystem being designed should be reliable enough to handle PQ anomalies. A PQ test results in an enormous amount of data for analysis with millions of data samples depending on the test and can be identified as big data. The engineer needs to analyze each set of test data as part of post-processing to ensure the power disturbances during testing are as per the standard requirements and that the functional performance of the subsystem is met. Manually analyzing the test data with classical methods and generating the report requires more time and effort, as millions of test data samples need to be analyzed, with the possibility of errors. Hence it is required to have automated methods to perform unsupervised data analysis in a faster way and generate the post-processed report as required by the end user. This article presents the testing of aircraft subsystem, for PQ with various power input disturbances involved, mainly focusing on test data analysis techniques using script-based analysis tools. Also, it discusses the utilization of options available in various tools for automation of test data analysis and presentation.
Meta TagsDetails
DOI
https://doi.org/10.4271/01-11-02-0008
Pages
10
Citation
Ramanjani, S., "Power Quality Test Data Analysis for Aircraft Subsystem," SAE Int. J. Aerosp. 11(2):127-139, 2018, https://doi.org/10.4271/01-11-02-0008.
Additional Details
Publisher
Published
Dec 21, 2018
Product Code
01-11-02-0008
Content Type
Journal Article
Language
English