Novel approach for improving power-plant availability using advanced engine diagnostics

Stephen Ogaji, Suresh Sampath, Riti Singh and Douglas Probert

Applied Energy, 2002, vol. 72, issue 1, 389-407

Abstract: Technological advances and high cost of ownership have resulted in considerable interest in advanced maintenance techniques. Quantifying fault and consequently availability requires the use of gas-turbine and combined-cycle models able to undertake appropriate diagnostics and life-cycle costing. These are complex processes as they include the simulation of such issues as performance and assessment of degraded gas-turbines, life usage and risk analysis. This report describes how the recent developments in engine diagnostics using advanced techniques like Artificial Neural Network (ANN) and Genetic Algorithm (GA) based techniques have provided new opportunities in the field of engine-fault diagnostics. It also discusses the potential of advanced engine-diagnostics, employing such features as ANN and GA for contributing to the management of availability of industrial gas-turbines.

Keywords: Diagnostics; Simulation; Availability; Artificial; intelligence (search for similar items in EconPapers)
Date: 2002
References: View complete reference list from CitEc
Citations: View citations in EconPapers (10)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0306-2619(02)00018-1
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:appene:v:72:y:2002:i:1:p:389-407

Ordering information: This journal article can be ordered from
http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/bibliographic
http://www.elsevier. ... 405891/bibliographic

Access Statistics for this article

Applied Energy is currently edited by J. Yan

More articles in Applied Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().