Managing Fleet Wide Sensory Data: Lessons Learned in Dealing with Volume, Velocity, Variety, Veracity, Value and Visibility



Preston Johnson


More than ever, asset operators and OEMs are investing in fleetwide monitoring systems. With the roll out of these monitoring systems, huge amounts of sensory data are generated. In a single Gigawatt power plant, asset monitoring systems sort through terabytes of sensory data per week. To contend with the volume and velocity of sensory data, analytics and data management techniques are employed along the life of sensory data from digitization at the asset, to storage in the information technology infrastructure. This paper presents techniques, both promising and fielded, for analytics to manage the volume, velocity, veracity, variety, and value of fleetwide asset monitoring data yielding opportunities for advanced visibility of actionable information.

How to Cite

Johnson, P. . (2014). Managing Fleet Wide Sensory Data: Lessons Learned in Dealing with Volume, Velocity, Variety, Veracity, Value and Visibility. Annual Conference of the PHM Society, 6(1).
Abstract 2 | PDF Downloads 1



vibration, Big Data, Fleet Wide Monitoring

Bisciglia , C. (2009). 5 Common Questions About Apache Hadoop. Cloudera Blog, 14 May 2009.

Bradicich, T. & Orci, S. (2012). Moore’s Law of Big Data National Instruments Instrumentation News. December 2012. Web.

Center for Intelligent Maintenance Systems (IMS), (2012). IMS Center brochure. IMS Center website. 5 Aug 2012. d_final.pdf

Cook, B. (2013). Deploying smart maintenance and diagnostics for electrical power generation. NIWeek 2013. 7 Aug 2012.

Franklin, C. (2012). Big Data as part of an enterprise data strategy. Tamgroup Blog. 19 March, 2012 as-part-of-an-enterprise-data-strategy

Gantz, J., & Reinsel, D.(2011). Extracting value from chaos. EMC Corporation website. June 2011. Web. Hadhazy, A. (2012). Zettabytes now needed to describe
global data overhead. Live Science. 4 May 2010 Web. Hessler, S. & Noce, G. “New web applications in EPRI’s PLantView software offer Progress Energy enhanced capabilities for using plant data”, publication 1021286, Electrical Power Research Institute, Palo Alto,California, USA

Hollingshaus, B. (2011). Program 69: Maintenance management and technology. Electrical Power Research Institute Descriptions of Past Research. Catalog number 1022681, May. 2011

Johnson, P. & Douglas F. (2011). The impact of rapidly changing computing technologies on prognostic asset management applications. MFPT Newsletter.
November 2011.Web.

Losito, R. (2011). World’s largest particle accelerator” National Instruments Case Study. 2011. Web.

Rogers, S. (2011). Big data is scaling BI and analytics. Information Management. 1 Sep 2011.
Technical Papers