Fleet Wide Asset Monitoring: Sensory Data to Signal Processing to Prognostics
##plugins.themes.bootstrap3.article.main##
##plugins.themes.bootstrap3.article.sidebar##
Abstract
Next generation fleet wide asset monitoring solutions are incorporating machine failure prediction and prognostics technologies. These technologies build on signal processing of vibration time waveforms, process parameters, and operating conditions of the machine. For prognostics algorithms to work well, the signal processing algorithms need to be applied correctly and the results need to be reliable. This paper provides a survey of signal processing techniques as applied to specific machine component with a focus on the output and use with prognostics technologies. With properly organized outputs, prognostics algorithms transform the fleet condition and health management challenge into a deployable fleet health management solution. To arrive at the deployable fleet management solution, a systematic approach in the design of the prognostics system is preferable. This approach includes data and model driven failure patterns, sensory data connectivity from deployed assets, prognostics analytical applications, and advisory generation outputs which guide the asset owners and maintainers.
How to Cite
##plugins.themes.bootstrap3.article.details##
data driven prognostics, PHM system design and engineering, vibration monitoring, Fleet
Reliability Hotwire (2004), Reliability Basics, Reliability HotWire, Issue 46, December 2004, http://www.weibull.com/hotwire/issue46/relbasics46.htm
Center for Advanced Life Cycle Engineering (CALCE), University of Maryland (2012). Introduction http://www.prognostics.umd.edu/tutorials.htm
National Aerospace and Space Administration (NASA), (2012). Data-Driven Prognostics http://ti.arc.nasa.gov/tech/dash/pcoe/data-driven- prognostics
Lei, Y., Djurdjanovic, D., Workman, G., Xiao, G. and Lee, J. (2004) Basic prognostics in industrial automation systems., Proceedings of the 6th International Conference on Frontiers of Design and Manufacturing. June 21-23, Xi'an, China
Sankavaram, C., Pattipati, B., Kodali, A., Pattipati, K., Azam, M., Kumar, S., and Pecht, M. (2009), Model-based and Data-driven Prognosis of Automotive and Electronic Systems.
Proceedings of 5th Annual IEEE Conference on Automation Science and Engineering., August 22-25, Bangalore, India http://www.prognostics.umd.edu/calcepapers/09_C haitanyaSankavaram_prgnosis_automativeElecsyst ems.pdf
Hollingshaus, B. (2011). Program 69: Maintenance Management and Technology. Electrical Power Research Institute Descriptions of Past Research. Catalog number 1022681, May. 2011 www.epri.com
Hussey, A., Hesler, S., and Bickford, R. (2010). Automation of Troubleshooting and Diagnostics of Power Plant Equipment Faults. Proceedings of 53rd ISA POWID Controls & Instrumentation Conference. 6–11 June, Summerlin, Nevada
Bechhoefer, E., and He, D., (2012) A Process for Data Driven Prognostics. Proceedings of the Prognostics and Health Management Solutions Conference. 24-26 April, Dayton, OH
Jayaswal, P., Wadhwani, A. and Mulchandani, A. (2008). Machine Fault Signature Analysis. International Journal of Rotating Machinery. Volume 2008, Article ID 583982, 10 pages doi:10.1155/2008/583982
Zhang, N. (2008) Advanced Signal Processing Algorithms and Architectures for Sound and Vibration. National Instruments NI-Week Conference. Presentation TS 1577. August 4-8, Austin, Texas
Johnson, E. and Johnson, P. (2012) Fleet Wide Asset Monitoring, Status Report from Progress Energy.
EPRI Combined CBM Meeting, July 16-20, San Diego, California
Cook, B. (2012) Smart Monitoring and Diagnostics for
Power Generation. National Instruments NIWeek conference. August 7-9, Austin, Texas
The Prognostic and Health Management Society advocates open-access to scientific data and uses a Creative Commons license for publishing and distributing any papers. A Creative Commons license does not relinquish the author’s copyright; rather it allows them to share some of their rights with any member of the public under certain conditions whilst enjoying full legal protection. By submitting an article to the International Conference of the Prognostics and Health Management Society, the authors agree to be bound by the associated terms and conditions including the following:
As the author, you retain the copyright to your Work. By submitting your Work, you are granting anybody the right to copy, distribute and transmit your Work and to adapt your Work with proper attribution under the terms of the Creative Commons Attribution 3.0 United States license. You assign rights to the Prognostics and Health Management Society to publish and disseminate your Work through electronic and print media if it is accepted for publication. A license note citing the Creative Commons Attribution 3.0 United States License as shown below needs to be placed in the footnote on the first page of the article.
First Author et al. This is an open-access article distributed under the terms of the Creative Commons Attribution 3.0 United States License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.