0

Full Content is available to subscribers

Subscribe/Learn More  >

Developing Predictive Maintenance Expertise to Improve Plant Equipment Reliability

[+] Author Affiliations
Richard N. Wurzbach

Maintenance Reliability Group, LLC, Brogue, PA

Paper No. ICONE10-22032, pp. 11-18; 8 pages
doi:10.1115/ICONE10-22032
From:
  • 10th International Conference on Nuclear Engineering
  • 10th International Conference on Nuclear Engineering, Volume 1
  • Arlington, Virginia, USA, April 14–18, 2002
  • Conference Sponsors: Nuclear Engineering Division
  • ISBN: 0-7918-3595-2 | eISBN: 0-7918-3589-8
  • Copyright © 2002 by ASME

abstract

On-line equipment condition monitoring is a critical component of the world-class production and safety histories of many successful nuclear plant operators. From addressing availability and operability concerns of nuclear safety-related equipment to increasing profitability through support system reliability and reduced maintenance costs, Predictive Maintenance programs have increasingly become a vital contribution to the maintenance and operation decisions of nuclear facilities. In recent years, significant advancements have been made in the quality and portability of many of the instruments being used, and software improvements have been made as well. However, the single most influential component of the success of these programs is the impact of a trained and experienced team of personnel putting this technology to work. Changes in the nature of the power generation industry brought on by competition, mergers, and acquisitions, has taken the historically stable personnel environment of power generation and created a very dynamic situation. As a result, many facilities have seen a significant turnover in personnel in key positions, including predictive maintenance personnel. It has become the challenge for many nuclear operators to maintain the consistent contribution of quality data and information from predictive maintenance that has become important in the overall equipment decision process. These challenges can be met through the implementation of quality training to predictive maintenance personnel and regular updating and re-certification of key technology holders. The use of data management tools and services aid in the sharing of information across sites within an operating company, and with experts who can contribute value-added data management and analysis. The overall effectiveness of predictive maintenance programs can be improved through the incorporation of newly developed comprehensive technology training courses. These courses address the use of key technologies such as vibration analysis, infrared thermography, and oil analysis not as singular entities, but as a toolbox resource from which to address overall equipment and plant reliability in a structured program and decision environment.

Copyright © 2002 by ASME

Figures

Tables

Interactive Graphics

Video

Country-Specific Mortality and Growth Failure in Infancy and Yound Children and Association With Material Stature

Use interactive graphics and maps to view and sort country-specific infant and early dhildhood mortality and growth failure data and their association with maternal

NOTE:
Citing articles are presented as examples only. In non-demo SCM6 implementation, integration with CrossRef’s "Cited By" API will populate this tab (http://www.crossref.org/citedby.html).

Some tools below are only available to our subscribers or users with an online account.

Related Content

Customize your page view by dragging and repositioning the boxes below.

Related eBook Content
Topic Collections

Sorry! You do not have access to this content. For assistance or to subscribe, please contact us:

  • TELEPHONE: 1-800-843-2763 (Toll-free in the USA)
  • EMAIL: asmedigitalcollection@asme.org
Sign In