Neural-based remote health assessment and failure diagnostics

Rahmat A. Shoureshi, Benjamin Terry

Research output: Contribution to conferencePaper

Abstract

As industrial competition intensifies, and more emphasis is placed on increased productivity and cost reduction, the development of a means to automatically monitor and assess the health of plant equipment becomes more important. This paper presents results of our research that integrates advances in the use of feature extraction, artificial neural networks, and communication/networking techniques for remote health assessment and failure diagnostics of laboratory and industrial rotating machinery. The key features of this effort are the ability to use internet for remote diagnostics, and combined Welch's transformation and neural networks for signature creation and analysis.

Original languageEnglish (US)
Pages1035-1042
Number of pages8
StatePublished - 2001
Event2001 ASME International Mechanical Engineering Congress and Exposition - New York, NY, United States
Duration: Nov 11 2001Nov 16 2001

Conference

Conference2001 ASME International Mechanical Engineering Congress and Exposition
CountryUnited States
CityNew York, NY
Period11/11/0111/16/01

ASJC Scopus subject areas

  • Engineering(all)

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  • Cite this

    Shoureshi, R. A., & Terry, B. (2001). Neural-based remote health assessment and failure diagnostics. 1035-1042. Paper presented at 2001 ASME International Mechanical Engineering Congress and Exposition, New York, NY, United States.