Journal article
Extreme learning machine - radial basis function (ELM-RBF) networks for diagnosing faults in a steam turbine
Journal of industrial and production engineering, Vol.39(7), pp.572-580
2022
DOI: 10.1080/21681015.2021.1887948
Abstract
A fast and reliable fault diagnosis system for a steam turbine in thermal power plant is crucial. The system will detect and classify a potential or occurring fault, hence suitable precautions steps will be correctly determined, and unplanned breakdown will be prevented. This study proposes a new application of extreme learning machine-radial basis function networks (ELM-RBF) for steam turbine fault diagnosis system. ELM-RBF recently has been known for its extremely fast computation. The proposed system was tested with real fault historical data from a steam power plant in Jakarta. To evaluate the system performance, a comparison with backpropagation neural networks (BPNN) was conducted. Four scenarios using ELM-RBF and BPNN, with and without ReliefF for feature selection were designed. The results show high accuracy in almost all the scenarios tested. The BPNN shows better accuracy than ELM-RBF, however, ELM-RBF performs considerably faster computation than BPNN without significant decrease in accuracy.
Details
- Title: Subtitle
- Extreme learning machine - radial basis function (ELM-RBF) networks for diagnosing faults in a steam turbine
- Creators
- Arian Dhini - University of IndonesiaIsti Surjandari - University of IndonesiaBenyamin Kusumoputro - University of IndonesiaAndrew Kusiak - University of Iowa
- Resource Type
- Journal article
- Publication Details
- Journal of industrial and production engineering, Vol.39(7), pp.572-580
- Publisher
- Taylor & Francis
- DOI
- 10.1080/21681015.2021.1887948
- ISSN
- 2168-1015
- eISSN
- 2168-1023
- Grant note
- name: Ministry of Research, Technology and Higher Education of Indonesia, award: 120/SP2H/PTNBH /DRPM/2018
- Language
- English
- Electronic publication date
- 03/01/2021
- Date published
- 2022
- Academic Unit
- Nursing; Industrial and Systems Engineering
- Record Identifier
- 9984186953202771
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