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Effective Automatic Expert Systems for Dynamic Predictive Maintenance Applications FREE

[+] Author Affiliations
Michael A. Flanagan, Carsten Andersson, Peter Surland

Brüel & Kjær CMS A/S, Naerum, Denmark

Paper No. 97-GT-064, pp. V004T14A012; 8 pages
doi:10.1115/97-GT-064
From:
  • ASME 1997 International Gas Turbine and Aeroengine Congress and Exhibition
  • Volume 4: Manufacturing Materials and Metallurgy; Ceramics; Structures and Dynamics; Controls, Diagnostics and Instrumentation; Education; IGTI Scholar Award
  • Orlando, Florida, USA, June 2–5, 1997
  • Conference Sponsors: International Gas Turbine Institute
  • ISBN: 978-0-7918-7871-2
  • Copyright © 1997 by ASME

abstract

As the need for specialist decision-making has increased with the volume of data produced by modern monitoring systems, and the current trend towards downsizing and external sourcing (for example, specialist consultant companies) has continued, the demand for computer-based expert systems for automatic machine condition (vibration and process) analysis and diagnosis has intensified.

Various levels of success have been achieved, but most expert systems available today do not reflect the actual reasoning process of a human expert; are inherently obsolete for the continuous learning capability required for dynamic applications; and/or require considerable skills in computer simulation or statistical methods to update the system.

In this paper, new techniques and tools are presented that address the basic elements in the reasoning process of a human expert, and offer solutions to the practical implementation of effective and reliable automatic machine diagnosis. Essential tools for optimum automatic spectrum analysis are first introduced, and then a method presented that allows system results to be automatically qualified and improved upon to reflect actual machine conditions. The paper then introduces neural-network technology as a means of implementing a workable, user-defined knowledge base that can be used to augment the expert system with the user’s own knowledge and experience, and the idiosyncrasies of individual machines.

Copyright © 1997 by ASME
This article is only available in the PDF format.

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