@techreport{byington_prognostic_2003,
	title = {Prognostic {Enhancements} to {Gas} {Turbine} {Diagnostic} {Systems}},
	url = {https://apps.dtic.mil/docs/citations/ADA457841},
	abstract = {The development of machinery health monitoring technologies has taken center stage within the DoD community in recent years. Existing health monitoring systems, such as the Integrated Condition Assessment System (ICAS) for NAVSEA, enable the diagnosis of mission critical problems using fault detection and diagnostic technologies. These technologies, however, have not specifically focused on the automated prediction of future condition (prognostics) of a machine based on the current diagnostic state of the machinery and its available operating and failure history data. Current efforts are focused on developing a generic architecture for the development of prognostic systems that will enable plug and play capabilities within existing systems. The designs utilize Open System Architecture (OSA) guidelines, such as OSA-CBM (Condition Based Maintenance), to provide these capabilities and enhance reusability of the software modules. One such implementation, which determines the optimal water wash interval to mitigate gas turbine compressor performance degradation due to salt deposit ingestion, is the focus of this paper. The module utilizes advanced probabilistic modeling and analysis technologies to forecast the future performance characteristics of the compressor and yield the optimal Time To Wash (TTW) from a cost/benefit standpoint. This paper describes the developed approach and architecture for developing prognostics using the gas turbine module.},
	language = {en},
	urldate = {2020-03-31},
	institution = {IMPACT TECHNOLOGIES LLC STATE COLLEGE PA},
	author = {Byington, Carl S. and Watson, Matthew and Roemer, Michael J. and Galie, Thomas R. and McGroarty, Jack J. and Savage, Christopher},
	month = jan,
	year = {2003},
}

{"_id":"oaupPQRq7A5c7amSZ","bibbaseid":"byington-watson-roemer-galie-mcgroarty-savage-prognosticenhancementstogasturbinediagnosticsystems-2003","author_short":["Byington, C. S.","Watson, M.","Roemer, M. J.","Galie, T. R.","McGroarty, J. J.","Savage, C."],"bibdata":{"bibtype":"techreport","type":"techreport","title":"Prognostic Enhancements to Gas Turbine Diagnostic Systems","url":"https://apps.dtic.mil/docs/citations/ADA457841","abstract":"The development of machinery health monitoring technologies has taken center stage within the DoD community in recent years. Existing health monitoring systems, such as the Integrated Condition Assessment System (ICAS) for NAVSEA, enable the diagnosis of mission critical problems using fault detection and diagnostic technologies. These technologies, however, have not specifically focused on the automated prediction of future condition (prognostics) of a machine based on the current diagnostic state of the machinery and its available operating and failure history data. Current efforts are focused on developing a generic architecture for the development of prognostic systems that will enable plug and play capabilities within existing systems. The designs utilize Open System Architecture (OSA) guidelines, such as OSA-CBM (Condition Based Maintenance), to provide these capabilities and enhance reusability of the software modules. One such implementation, which determines the optimal water wash interval to mitigate gas turbine compressor performance degradation due to salt deposit ingestion, is the focus of this paper. The module utilizes advanced probabilistic modeling and analysis technologies to forecast the future performance characteristics of the compressor and yield the optimal Time To Wash (TTW) from a cost/benefit standpoint. This paper describes the developed approach and architecture for developing prognostics using the gas turbine module.","language":"en","urldate":"2020-03-31","institution":"IMPACT TECHNOLOGIES LLC STATE COLLEGE PA","author":[{"propositions":[],"lastnames":["Byington"],"firstnames":["Carl","S."],"suffixes":[]},{"propositions":[],"lastnames":["Watson"],"firstnames":["Matthew"],"suffixes":[]},{"propositions":[],"lastnames":["Roemer"],"firstnames":["Michael","J."],"suffixes":[]},{"propositions":[],"lastnames":["Galie"],"firstnames":["Thomas","R."],"suffixes":[]},{"propositions":[],"lastnames":["McGroarty"],"firstnames":["Jack","J."],"suffixes":[]},{"propositions":[],"lastnames":["Savage"],"firstnames":["Christopher"],"suffixes":[]}],"month":"January","year":"2003","bibtex":"@techreport{byington_prognostic_2003,\n\ttitle = {Prognostic {Enhancements} to {Gas} {Turbine} {Diagnostic} {Systems}},\n\turl = {https://apps.dtic.mil/docs/citations/ADA457841},\n\tabstract = {The development of machinery health monitoring technologies has taken center stage within the DoD community in recent years. Existing health monitoring systems, such as the Integrated Condition Assessment System (ICAS) for NAVSEA, enable the diagnosis of mission critical problems using fault detection and diagnostic technologies. These technologies, however, have not specifically focused on the automated prediction of future condition (prognostics) of a machine based on the current diagnostic state of the machinery and its available operating and failure history data. Current efforts are focused on developing a generic architecture for the development of prognostic systems that will enable plug and play capabilities within existing systems. The designs utilize Open System Architecture (OSA) guidelines, such as OSA-CBM (Condition Based Maintenance), to provide these capabilities and enhance reusability of the software modules. One such implementation, which determines the optimal water wash interval to mitigate gas turbine compressor performance degradation due to salt deposit ingestion, is the focus of this paper. The module utilizes advanced probabilistic modeling and analysis technologies to forecast the future performance characteristics of the compressor and yield the optimal Time To Wash (TTW) from a cost/benefit standpoint. This paper describes the developed approach and architecture for developing prognostics using the gas turbine module.},\n\tlanguage = {en},\n\turldate = {2020-03-31},\n\tinstitution = {IMPACT TECHNOLOGIES LLC STATE COLLEGE PA},\n\tauthor = {Byington, Carl S. and Watson, Matthew and Roemer, Michael J. and Galie, Thomas R. and McGroarty, Jack J. and Savage, Christopher},\n\tmonth = jan,\n\tyear = {2003},\n}\n\n\n\n","author_short":["Byington, C. S.","Watson, M.","Roemer, M. J.","Galie, T. R.","McGroarty, J. J.","Savage, C."],"key":"byington_prognostic_2003","id":"byington_prognostic_2003","bibbaseid":"byington-watson-roemer-galie-mcgroarty-savage-prognosticenhancementstogasturbinediagnosticsystems-2003","role":"author","urls":{"Paper":"https://apps.dtic.mil/docs/citations/ADA457841"},"metadata":{"authorlinks":{}},"html":""},"bibtype":"techreport","biburl":"https://bibbase.org/zotero/mh_lenguyen","dataSources":["iwKepCrWBps7ojhDx"],"keywords":[],"search_terms":["prognostic","enhancements","gas","turbine","diagnostic","systems","byington","watson","roemer","galie","mcgroarty","savage"],"title":"Prognostic Enhancements to Gas Turbine Diagnostic Systems","year":2003}