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Quantitative approach to risk based maintenance decisions

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conference contribution
posted on 2017-12-06, 00:00 authored by Gopinath Chattopadhyay, Malcolm Leinster
Maintenance is an important activity for asset intensive industries. It enhances life and reduces operating risks of plant and equipment. Effectiveness of maintenance is reflected in the bottom line of organisations through reduced costs of operation, downtime, injuries, repair and replacements, asset loss and insurance premiums. It is difficult to accurately predict the degradation and wear and tear of long life assets. Accurate cost of risks linked to injuries and compensation is another complex area for quantification. Understanding of the magnitude of risk in financial terms makes it easier for decision makers to use executive judgement for choosing appropriate design from available alternatives and/or retrospective plant and process modifications. Quantitative risk models are helpful for cost effective operational and maintenance decisions. This paper is focused on how the failure characteristics of a component or assembly can be modelled mathematically. Quantitative approach to risk based maintenance decisions is proposed to estimate risks associated with failures and how to evaluate effectiveness of risk mitigation using alternative strategies

Funding

Category 4 - CRC Research Income

History

Parent Title

Proceedings of the 22nd International congress on Condition monitoring and diagnostic engineering management, COMADEM 2009, 9-11 June 2009, San Sebastian, Spain.

Start Page

141

End Page

148

Number of Pages

8

Start Date

2009-01-01

ISBN-13

9788493206468

Location

San Sebastian, Spain

Publisher

Fundaci n TEKNIKER

Place of Publication

Otaola, 20, 20600 Eibar

Peer Reviewed

  • Yes

Open Access

  • No

External Author Affiliations

Institute for Resource Industries and Sustainability (IRIS); Process Engineering and Light Metals; TBA Research Institute;

Era Eligible

  • Yes

Name of Conference

International Congress on Condition Monitoring and Diagnostic Engineering Management

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