Expert Systems with Applications: An International Journal
Multi-stage extreme learning machine for fault diagnosis on hydraulic tube tester
Neural Computing and Applications - Special Issue: Neural networks for control, robotics and diagnostics
Statistics over features of ECG signals
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Multiway Spectral Clustering with Out-of-Sample Extensions through Weighted Kernel PCA
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fault diagnosis of ball bearings using machine learning methods
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Application of extreme learning machine for series compensated transmission line protection
Engineering Applications of Artificial Intelligence
Engineering Applications of Artificial Intelligence
A Semiautomatic Approach to Deriving Turbine Generator Diagnostic Knowledge
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
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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.