کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
408151 678250 2014 9 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Real-time fault diagnosis for gas turbine generator systems using extreme learning machine
ترجمه فارسی عنوان
تشخیص خطا در زمان واقعی برای سیستم های ژنراتور توربین گاز با استفاده از دستگاه یادگیری افراطی
کلمات کلیدی
تشخیص خطا در زمان واقعی، سیستم ژنراتور توربین گاز، دستگاه یادگیری شدید تبدیل بسته ویولت، ویژگی های آماری زمان دامنه، تجزیه و تحلیل مولفه اصلی هسته
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر هوش مصنوعی
چکیده انگلیسی

Real-time fault diagnostic system is very important to maintain the operation of the gas turbine generator system (GTGS) in power plants, where any abnormal situation will interrupt the electricity supply. The GTGS is complicated and has many types of component faults. To prevent from interruption of electricity supply, a reliable and quick response framework for real-time fault diagnosis of the GTGS is necessary. As the architecture and the learning algorithm of extreme learning machine (ELM) are simple and effective respectively, ELM can identify faults quickly and precisely as compared with traditional identification techniques such as support vector machines (SVM). This paper therefore proposes a new application of ELM for building a real-time fault diagnostic system in which data pre-processing techniques are integrated. In terms of data pre-processing, wavelet packet transform and time-domain statistical features are proposed for extraction of vibration signal features. Kernel principal component analysis is then applied to further reduce the redundant features in order to shorten the fault identification time and improve accuracy. To evaluate the system performance, a comparison between ELM and the prevailing SVM on the fault detection was conducted. Experimental results show that the proposed diagnostic framework can detect component faults much faster than SVM, while ELM is competitive with SVM in accuracy. This paper is also the first in the literature that explores the superiority of the fault identification time of ELM.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Neurocomputing - Volume 128, 27 March 2014, Pages 249–257
نویسندگان
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