Logo

Orthonormal Basis Filters for gas turbine fault diagnostics system design: A review

Tamiru, A.L. and Fakhruldin, M.H. and Mohd. Amin, A.M. and Ainul, A.M. (2016) Orthonormal Basis Filters for gas turbine fault diagnostics system design: A review. ARPN Journal of Engineering and Applied Sciences, 11 (22). pp. 13399-13404.

Full text not available from this repository.

Official URL: https://www.scopus.com/inward/record.uri?eid=2-s2....

Abstract

Gas turbines have become the dominant technology for power generation. They can be quickly assembled and put to service. They are convenient for engine exchange during system overhaul. The emission of NOx, SOx, CO, and particulates are also significantly law as compared to coal fired power plants. However, their maintenance cost is relatively high. The perceived best approach to reduce the cost is by using a proactive maintenance strategy in which a real-time diagnostics system plays a key role. The purpose of this paper is to review application of Orthonormal Basis Filters (OBFs) to fault detection and diagnostic systems design. The types of OBFs studied include Laguerre filters, Meixner filters, Kaurtz filters, Generalized OBF, and Markov-OBF. The combination of OBFs and computational intelligence methods (artificial neural network, fuzzy systems, and evolutionary optimization) are also highlighted. The review shows that, even though OBFs have been around for more than a decade, their application is limited to model identification only. As such, the only diagnostic problem revealed so far is that concentrating on stirred tank reactor. Therefore, to extend the use of OBFs to power plants, there needs to be further study in the context of power plants or specifically gas turbines. © 2006-2016 Asian Research Publishing Network (ARPN).

Item Type:Article
Impact Factor:cited By 1
ID Code:25372
Deposited By: Ms Sharifah Fahimah Saiyed Yeop
Deposited On:27 Aug 2021 12:59
Last Modified:27 Aug 2021 12:59

Repository Staff Only: item control page