October 2018
Intermediate
800 pages
28h 49m
English
Content preview from Prognostics and Health Management of Electronics
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19 PHM Software for Electronics
Noel Jordan Jameson1, Myeongsu Kang2 and Jing Tian3
1 National Institute of Standards and Technology, Gaithersburg, MD, USA
2 University of Maryland, Center for Advanced Life Cycle Engineering, College Park, MD, USA
3 The DEI Group, Baltimore, MD, USA
Prognostics and systems health management (PHM) is a multifaceted approach to protect the integrity of systems and avoid unanticipated operational problems leading to mission performance deficiencies, degradation, and adverse effects on mission safety. To enable PHM, physics‐of‐failure (PoF) and data‐driven approaches have been studied. The PoF‐based approach deals with the application of first principles models to understand the various failure mechanisms and thereby predict the remaining useful life (RUL) and reliability of electronic systems/components.
Until now, data‐driven PHM is mainly implemented using programming languages such as MATLAB, Python, and R. Learning these programming languages requires much effort, and implementing PHM methods can be time‐consuming. Cloud‐based platforms such as Microsoft Azure and Amazon AWS provide algorithms that can be used in PHM, but they are general machine learning tools that are not readily optimized for PHM applications, and users need much knowledge of both the platform and PHM to use them. Companies such as PHM Technology and Impact Technologies provided tools for specific PHM tasks and related consulting services, but they do not provide ...
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