Abstract
There are many facets of a prognostics and health management system. Facets include data collection systems that monitor machine parameters; signal processing facilities that sort, analyze, and extract features from collected data; pattern matching algorithms that work to identify machine degradation modes; database systems that organize, trend, compare, and report information; communications that synchronize prognostic system information with business functions including plant operations; and finally visualization features that allow interested personnel the ability to view data, reports, and information from within the intranet or across the Internet. A prognostic system includes all of these facets, with details of each varying to match specific needs of specific machinery. To profitably commercialize a prognostic system, a generic yet flexible framework is adopted which allows customization of individual facets. Customization of one facet does not materially impact another. This chapter describes the framework, customization process, and choices of commercial system components.
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References
International Standards Organization (2003) Condition monitoring and diagnostics of machines—data processing, communication and presentation—part 1: general guidelines. ISO-13374-1:2003 Available from www.iso.org
Lee J (2009) Advanced prognostics for smart systems. Intelligent maintenance systems (IMS) introduction. Available from www.imscenter.net. p. 6–8
Intelligent Maintenance Systems (2007) Watchdog agent® documentation, center for Intelligent Maintenance Systems. Available from www.imscenter.net
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© 2014 Springer-Verlag London
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Johnson, P. (2014). Commercialization of Prognostics Systems Using Commercial Off-the-Shelf Technologies. In: Lee, J., Ni, J., Sarangapani, J., Mathew, J. (eds) Engineering Asset Management 2011. Lecture Notes in Mechanical Engineering. Springer, London. https://doi.org/10.1007/978-1-4471-4993-4_19
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DOI: https://doi.org/10.1007/978-1-4471-4993-4_19
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