Proficy Advanced Analytics: a Case Study for Real World PHM Application in Energy

##plugins.themes.bootstrap3.article.main##

##plugins.themes.bootstrap3.article.sidebar##

Published Sep 25, 2011
Subrat Nanda Xiaohui Hu

Abstract

GE monitors a large number of heavy duty equipment for energy generation, locomotives and aviation. These monitoring and diagnostic centers located world-wide sense, derive, transmit, analyze and view terabytes of sensory and calculated data each year. This is used to arrive at critical decisions pertaining to equipment life management - like useful life estimation, inventory planning and finally assuring a minimum level of performance to GE customers. Although a large number of analytical tools exist in today’s market, however, there is a need to have a tool at disposal which can aid not just in the analytical algorithms and data processing but also a platform for fleet wide deployment, monitoring and online processing of equipment. We describe a Prognostics & Health Management (PHM) application for GE Energy which was implemented using GE Intelligent Platform products and explore some capabilities of both the application and the analytics tool.

How to Cite

Nanda, S. ., & Hu, X. . (2011). Proficy Advanced Analytics: a Case Study for Real World PHM Application in Energy. Annual Conference of the PHM Society, 3(1), 2011. https://doi.org/10.36001/phmconf.2011.v3i1.1988
Abstract 223 | PDF Downloads 138

##plugins.themes.bootstrap3.article.details##

Keywords

condition monitoring, data preprocessing, Data Acquisition, Data-driven and model-based prognostics

References
GE Intelligent Platforms. http://www.ge-ip.com Mathworks: http://www.mathworks.com

CSense Systems (Pty) Ltd. User’s manual for Proficy Advanced Analytics 5.0 (2011)

Casella, G (1990); Berger R. L Statistical Inference.

Duda R.O (2001); Hart P.E; Stork, D. G: Pattern Classification. John Wiley & Sons.

Hu, Xiao(2007), Qui, Hai and Iyer, Naresh: Multivariate Change Detection for Time Series Data in Aircraft Engine Fault Diagnostics, IEEE.

M. Markou (2003), S. Singh, Novelty Detection : A Review Part 1: Statistical Approaches, Signal Processing, Vol. 83(12), pp2481-2497.

Kumar, Vipin et al(2005): An Introduction to Data Mining. Russel S(2002), Norvig P; Artificial Intelligence, A Modern Approach, Prentice Hall of India, 2nd edition.

Jammu, Vinay(2010), et al; Review of Prognostics and Health Management Technologies, GE Global Research Bonissone, P., & Kai Goebel, Soft Computing Techniques for Diagnostics and Prognostics

Ebeling (2005), C. E., An Introduction to Reliability and Maintainability Engineering, Tata McGraw-Hill Publishing Company, New Delhi.

Vachtsevanos(2006), G., Frank Lewis, Michael Roemer, Andrew Hess and Biqing Wu, Intelligent Fault Diagnosis and Prognosis for Engineering Systems, John Wiley & Sons, Hoboken, New Jersey, 2006
Section
Poster Presentations