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Fault diagnosis for a solar assisted heat pump system under incomplete data and expert knowledge

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  • Liu, Zengkai
  • Liu, Yonghong
  • Zhang, Dawei
  • Cai, Baoping
  • Zheng, Chao

Abstract

Fault diagnosis for a solar assisted heat pump (SAHP) system in the presence of incomplete data and expert knowledge is discussed in this article. A method for parameter learning of Bayesian networks (BNs) from incomplete data based on the back-propagation (BP) neural network and maximum likelihood estimation (MLE), which is called BP-MLE method, is presented. The BP neural network is utilized to impute the missing data and the complete data sets are addressed with MLE to obtain the parameters of BN. A method for parameter estimation under incomplete expert knowledge based on BP neural networks and fuzzy set theory is also presented, which is called BP-FS method. Similarly, the missing information is imputed by the trained BP neural network. Fuzzy set theory is employed to quantify the parameters of BN based on complete qualitative expert knowledge. The presented methods are applied to parameter learning of diagnostic BN for a SAHP system with incomplete simulation data and expert knowledge. The developed BN can perform fault diagnosis with complete or incomplete symptoms.

Suggested Citation

  • Liu, Zengkai & Liu, Yonghong & Zhang, Dawei & Cai, Baoping & Zheng, Chao, 2015. "Fault diagnosis for a solar assisted heat pump system under incomplete data and expert knowledge," Energy, Elsevier, vol. 87(C), pages 41-48.
    • Handle: RePEc:eee:energy:v:87:y:2015:i:c:p:41-48
    DOI: 10.1016/j.energy.2015.04.090
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    1. Cai, Baoping & Liu, Yonghong & Fan, Qian & Zhang, Yunwei & Liu, Zengkai & Yu, Shilin & Ji, Renjie, 2014. "Multi-source information fusion based fault diagnosis of ground-source heat pump using Bayesian network," Applied Energy, Elsevier, vol. 114(C), pages 1-9.
    2. Mohanraj, M. & Jayaraj, S. & Muraleedharan, C., 2009. "Performance prediction of a direct expansion solar assisted heat pump using artificial neural networks," Applied Energy, Elsevier, vol. 86(9), pages 1442-1449, September.
    3. Li, Hong & Yang, Hongxing, 2010. "Study on performance of solar assisted air source heat pump systems for hot water production in Hong Kong," Applied Energy, Elsevier, vol. 87(9), pages 2818-2825, September.
    4. Chow, T.T. & Bai, Y. & Fong, K.F. & Lin, Z., 2012. "Analysis of a solar assisted heat pump system for indoor swimming pool water and space heating," Applied Energy, Elsevier, vol. 100(C), pages 309-317.
    5. Marquez, David & Neil, Martin & Fenton, Norman, 2010. "Improved reliability modeling using Bayesian networks and dynamic discretization," Reliability Engineering and System Safety, Elsevier, vol. 95(4), pages 412-425.
    6. Li, Hong & Yang, Hongxing, 2009. "Potential application of solar thermal systems for hot water production in Hong Kong," Applied Energy, Elsevier, vol. 86(2), pages 175-180, February.
    7. Najafi, Massieh & Auslander, David M. & Bartlett, Peter L. & Haves, Philip & Sohn, Michael D., 2012. "Application of machine learning in the fault diagnostics of air handling units," Applied Energy, Elsevier, vol. 96(C), pages 347-358.
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