Bayesian network analysis for diagnostics and prognostics of engineering systems
<p>Bayesian networks have been applied to many different domains to perform prognostics, reduce risk and ultimately improve decision making. However, these methods have not been applied to military field and human performance data sets in an industrial environment. Methods frequently rely on a...
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ndltd-MSSTATE-oai-library.msstate.edu-etd-04202017-1442042019-05-15T18:44:00Z Bayesian network analysis for diagnostics and prognostics of engineering systems Banghart, Marc Industrial and Systems Engineering <p>Bayesian networks have been applied to many different domains to perform prognostics, reduce risk and ultimately improve decision making. However, these methods have not been applied to military field and human performance data sets in an industrial environment. Methods frequently rely on a clear understanding of causal connections leading to an undesirable event and detailed understanding of the system behavior. Methods may also require large amount of analyst teams and domain experts, coupled with manual data cleansing and classification. The research performed utilized machine learning algorithms (such as Bayesian networks) and two existing data sets. The primary objective of the research was to develop a diagnostic and prognostic tool utilizing Bayesian networks that does not require the need for detailed causal understanding of the underlying system. The research yielded a predictive method with substantial benefits over reactive methods. The research indicated Bayesian networks can be trained and utilized to predict failure of several important components to include potential malfunction codes and downtime on a real-world Navy data set. The research also considered potential error within the training data set. The results provided credence to utilization of Bayesian networks in real field data which will always contain error that is not easily quantified. Research should be replicated with additional field data sets from other aircraft. Future research should be conducted to solicit and incorporate domain expertise into subsequent models. Research should also consider incorporation of text based analytics for text fields, which was considered out of scope for this research project. </p> Linkan Bian Andreas Tolk Kari Babski-Reeves Lesley Strawderman MSSTATE 2017-07-31 text application/pdf http://sun.library.msstate.edu/ETD-db/theses/available/etd-04202017-144204/ http://sun.library.msstate.edu/ETD-db/theses/available/etd-04202017-144204/ en unrestricted I hereby certify that, if appropriate, I have obtained and attached hereto a written permission statement from the owner(s) of each third party copyrighted matter to be included in my thesis, Dissertation, or project report, allowing distribution as specified below. I certify that the version I submitted is the same as that approved by my advisory committee. I hereby grant to Mississippi State University Libraries or its agents the non-exclusive license to archive and make accessible, under the conditions specified below, my thesis, Dissertation, or project report in whole or in part in all forms of media, now or hereafter known. I retain all other ownership rights to the copyright of the thesis, Dissertation or project report. I also retain the right to use in future works (such as articles or books) all or part of this thesis, Dissertation, or project report. |
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Industrial and Systems Engineering Banghart, Marc Bayesian network analysis for diagnostics and prognostics of engineering systems |
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<p>Bayesian networks have been applied to many different domains to perform prognostics, reduce risk and ultimately improve decision making. However, these methods have not been applied to military field and human performance data sets in an industrial environment. Methods frequently rely on a clear understanding of causal connections leading to an undesirable event and detailed understanding of the system behavior. Methods may also require large amount of analyst teams and domain experts, coupled with manual data cleansing and classification. The research performed utilized machine learning algorithms (such as Bayesian networks) and two existing data sets. The primary objective of the research was to develop a diagnostic and prognostic tool utilizing Bayesian networks that does not require the need for detailed causal understanding of the underlying system. The research yielded a predictive method with substantial benefits over reactive methods. The research indicated Bayesian networks can be trained and utilized to predict failure of several important components to include potential malfunction codes and downtime on a real-world Navy data set. The research also considered potential error within the training data set. The results provided credence
to utilization of Bayesian networks in real field data which will always contain error that is not easily quantified. Research should be replicated with additional field data sets from other aircraft. Future research should be conducted to solicit and incorporate domain expertise into subsequent models. Research should also consider incorporation of text based analytics for text fields, which was considered out of scope for this research project. </p> |
author2 |
Linkan Bian |
author_facet |
Linkan Bian Banghart, Marc |
author |
Banghart, Marc |
author_sort |
Banghart, Marc |
title |
Bayesian network analysis for diagnostics and prognostics of engineering systems |
title_short |
Bayesian network analysis for diagnostics and prognostics of engineering systems |
title_full |
Bayesian network analysis for diagnostics and prognostics of engineering systems |
title_fullStr |
Bayesian network analysis for diagnostics and prognostics of engineering systems |
title_full_unstemmed |
Bayesian network analysis for diagnostics and prognostics of engineering systems |
title_sort |
bayesian network analysis for diagnostics and prognostics of engineering systems |
publisher |
MSSTATE |
publishDate |
2017 |
url |
http://sun.library.msstate.edu/ETD-db/theses/available/etd-04202017-144204/ |
work_keys_str_mv |
AT banghartmarc bayesiannetworkanalysisfordiagnosticsandprognosticsofengineeringsystems |
_version_ |
1719086092834045952 |