University of Hertfordshire

By the same authors

A Knowledge Transfer Platform for Fault Diagnosis of Industrial Gas Turbines

Research output: Contribution to conferencePaperpeer-review

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A Knowledge Transfer Platform for Fault Diagnosis of Industrial Gas Turbines. / Zhang, Yu; Jombo, Gbanaibolou; Latimer, Anthony.

2018. 000347-000352 Paper presented at 22nd IEEE International Conference on Intelligent Engineering Systems, Canaria, Spain.

Research output: Contribution to conferencePaperpeer-review

Harvard

Zhang, Y, Jombo, G & Latimer, A 2018, 'A Knowledge Transfer Platform for Fault Diagnosis of Industrial Gas Turbines', Paper presented at 22nd IEEE International Conference on Intelligent Engineering Systems, Canaria, Spain, 21/06/18 pp. 000347-000352. https://doi.org/10.1109/INES.2018.8523864

APA

Zhang, Y., Jombo, G., & Latimer, A. (2018). A Knowledge Transfer Platform for Fault Diagnosis of Industrial Gas Turbines. 000347-000352. Paper presented at 22nd IEEE International Conference on Intelligent Engineering Systems, Canaria, Spain. https://doi.org/10.1109/INES.2018.8523864

Vancouver

Zhang Y, Jombo G, Latimer A. A Knowledge Transfer Platform for Fault Diagnosis of Industrial Gas Turbines. 2018. Paper presented at 22nd IEEE International Conference on Intelligent Engineering Systems, Canaria, Spain. https://doi.org/10.1109/INES.2018.8523864

Author

Zhang, Yu ; Jombo, Gbanaibolou ; Latimer, Anthony. / A Knowledge Transfer Platform for Fault Diagnosis of Industrial Gas Turbines. Paper presented at 22nd IEEE International Conference on Intelligent Engineering Systems, Canaria, Spain.6 p.

Bibtex

@conference{efca3da505334bf884f6fb4c71939c15,
title = "A Knowledge Transfer Platform for Fault Diagnosis of Industrial Gas Turbines",
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.",
author = "Yu Zhang and Gbanaibolou Jombo and Anthony Latimer",
note = "{\textcopyright} 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.; 22nd IEEE International Conference on Intelligent Engineering Systems ; Conference date: 21-06-2018",
year = "2018",
month = nov,
day = "8",
doi = "10.1109/INES.2018.8523864",
language = "English",
pages = "000347--000352",

}

RIS

TY - CONF

T1 - A Knowledge Transfer Platform for Fault Diagnosis of Industrial Gas Turbines

AU - Zhang, Yu

AU - Jombo, Gbanaibolou

AU - Latimer, Anthony

N1 - © 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.

PY - 2018/11/8

Y1 - 2018/11/8

N2 - 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.

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.

U2 - 10.1109/INES.2018.8523864

DO - 10.1109/INES.2018.8523864

M3 - Paper

SP - 347

EP - 352

T2 - 22nd IEEE International Conference on Intelligent Engineering Systems

Y2 - 21 June 2018

ER -