Failure Mode & Effect Analysis and another Methodology for Improving Data Veracity and Validity

Ana Elsa Hinojosa Herrera, Chris Walshaw, Chris Bailey
<span title="2020-07-01">2020</span> <i title="International Association for Educators and Researchers (IAER)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/xjw6kdfs6vck3oyp24amno6mza" style="color: black;">Annals of Emerging Technologies in Computing</a> </i> &nbsp;
Failure Mode & Effect Analysis (FMEA) is a method that has been used to improve reliability of products, processes, designs, and software for different applications. In this paper we extend its usage for data veracity and validity improvement in the context of big data analysis and discuss its application in an electronics manufacturing test procedure which consists of a sequence of tests. Finally, we describe another methodology, developed as a result of the DVV-FMEA application which is aimed
more &raquo; ... at improving the tests' repeatability and failure detection capabilities as well as monitoring their reliability.
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.33166/aetic.2020.03.002">doi:10.33166/aetic.2020.03.002</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/chdqec36grh2xhjmpsmrn2casy">fatcat:chdqec36grh2xhjmpsmrn2casy</a> </span>
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