A Knowledge Transfer Platform for Fault Diagnosis of Industrial Gas Turbines

Yu Zhang, Gbanaibolou Jombo, Anthony Latimer

Research output: Contribution to conferencePaperpeer-review

4 Citations (Scopus)
31 Downloads (Pure)

Abstract

The aim of this paper is to introduce the bases of an intelligent fault diagnostic platform, which assists in detecting mechanical failures of Industrial Gas Turbines (IGTs). This comprises an integration of an expert system and its complementary signal processing techniques. The essential characteristic here is not to exclude humans (experts) from the diagnostic process, but rather to transfer their knowledge and experience to a computerized platform. The automated process executed by the computerized platform is to ensure the scalability and consistency in fault diagnosis; while the humans are required to corroborate the transparency and liability of the outcomes. In this paper, a Knowledge Transfer Platform (KTP) is proposed for fault diagnosis of industrial systems. It is then designed and tested for combustion fault diagnosis using field data of IGTs. The preliminary results have revealed the feasibility and efficacy of the proposed scheme, which has the potential to be further extended to a large industrial scale and to different engineering diagnostic applications.
Original languageEnglish
Pages000347-000352
Number of pages6
DOIs
Publication statusPublished - 8 Nov 2018
Event22nd IEEE International Conference on Intelligent Engineering Systems - Canaria, Spain
Duration: 21 Jun 2018 → …

Conference

Conference22nd IEEE International Conference on Intelligent Engineering Systems
Country/TerritorySpain
CityCanaria
Period21/06/18 → …

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