A Knowledge Transfer Platform for Fault Diagnosis of Industrial Gas Turbines

Yu Zhang; Gbanaibolou Jombo; Anthony Latimer

doi:10.1109/INES.2018.8523864

A Knowledge Transfer Platform for Fault Diagnosis of Industrial Gas Turbines

Yu Zhang, Gbanaibolou Jombo, Anthony Latimer

Research output: Contribution to conference › Paper › peer-review

4 Citations (Scopus)

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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 language	English
Pages	000347-000352
Number of pages	6
DOIs	https://doi.org/10.1109/INES.2018.8523864
Publication status	Published - 8 Nov 2018
Event	22nd IEEE International Conference on Intelligent Engineering Systems - Canaria, Spain Duration: 21 Jun 2018 → …

Conference

Conference	22nd IEEE International Conference on Intelligent Engineering Systems
Country/Territory	Spain
City	Canaria
Period	21/06/18 → …

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10.1109/INES.2018.8523864Licence: Other

KTP_INES_2018
© 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Accepted author manuscript, 922 KBLicence: Unspecified

Cite this

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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.",

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AB - 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.

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A Knowledge Transfer Platform for Fault Diagnosis of Industrial Gas Turbines

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