Refrigerated Showcase Fault Detection by a Correntropy Based Artificial Neural Network Using Fast Brain Storm Optimization

Author:

Otaka Naoya,Fukuyama Yoshikazu,Kawamura Yu,Murakami Kenya,Santana Adamo,Iizaka Tatsuya,Matsui Tetsuro

Publisher

Springer International Publishing

Reference15 articles.

1. Santana, A., et al.: Machine learning application for refrigeration showcase fault discrimination. In: Proceedings of Region 10 Conference of IEEE (TENCON) (2016)

2. Rossi, T.M., et al.: A statistical, rule-based fault detection and diagnostic method for vapor compression air conditioners. HVAC&R Res. 3, 19–37 (1997)

3. Youk, S., et al: Fault detection system using directional classified rule-based in AHU. In: Proceedings of Future Generation Communication and Networking (FGCN) (2007)

4. Han, D., et al.: Automated fault diagnosis method for a variable air volume air handling unit. In: Proceedings of International Conference on Control, Automation and Systems - Society of Instrument and Control Engineers (ICCAS-SICE) (2009)

5. Li, H., et al.: Application of automated fault detection and diagnostics for rooftop air conditioners in California. In: Proceedings of 2004 ACEEE Summer Study on Energy Efficiency in Buildings, pp. 190–201 (2004)

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