A fault diagnosis model based on weighted extension neural network for turbo-generator sets on small samples with noise |
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Affiliation: | 1. College of Mechanical and Electrical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China;2. Department of Business Strategy and Innovation, Griffith University, Gold Coast Campus, QLD 4222, Australia;3. School of Business, Jiangsu Open University, Nanjing 210036, China |
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Abstract: | In data-driven fault diagnosis for turbo-generator sets, the fault samples are usually expensive to obtain, and inevitably with noise, which will both lead to an unsatisfying identification performance of diagnosis models. To address these issues, this paper proposes a fault diagnosis model for turbo-generator sets based on Weighted Extension Neural Network (W-ENN). W-ENN is a novel neural network which has three types of connection weights and an improved correlation function. The performance of the proposed model is validated against Extension Neural Network (ENN), Support Vector Machine (SVM), Relevance Vector Machine (RVM) and Extreme Learning Machine (ELM) based models. The results indicate that, on noisy small sample sets, the proposed model is superior to the other models in terms of higher identification accuracy with fewer samples and strong noise-tolerant ability. The findings of this study may serve as a powerful fault diagnosis model for turbo-generator sets on noisy small sample sets. |
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Keywords: | Fault diagnosis Samples with noise Small samples learning Turbo-generator sets Weighted Extension Neural Network |
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