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Fault detection and diagnosis of chillers using Bayesian network merged distance rejection and multi-source non-sensor information

Author

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  • Wang, Zhanwei
  • Wang, Zhiwei
  • He, Suowei
  • Gu, Xiaowei
  • Yan, Zeng Feng

Abstract

Applying the fault detection and diagnosis (FDD) techniques to chillers is beneficial to reduce building energy consumption and to enhance the energy efficiency of buildings. The purpose of this study is to propose a chiller FDD method with better performance for field implementation. The technological paths are as follows: (i) in order to identify new types of faults and to update the FDD fault libraries, a distance rejection (DR) technique is merged into the Bayesian network (BN) by transforming the chiller FDD problem into a single-class classification problem. Furthermore, the DR can be tuned to obtain an adjustable false alarm rate (FAR); (ii) to increase the diagnostic accuracies of known (or existing) faults and the identification accuracies of new types of faults, multi-source non-sensor information (MI) is merged into the BN, i.e., maintenance records and repair service history, healthy states of related equipment and on-site observed information. A novel chiller FDD method based on BN merged DR and MI (DR-MI-BN) is proposed in this study. The performance of this proposed method is evaluated by using the experimental data from ASHRAE RP-1043. Test results show that the FAR can be tuned for different users’ requirements, and that merging the MI significantly improves the diagnostic accuracies of known faults from 77.2% to 99.8% at most (for refrigerant leakage) and the identification accuracies of new types of faults from 56.6% to 99.6% at most (for NF7).

Suggested Citation

  • Wang, Zhanwei & Wang, Zhiwei & He, Suowei & Gu, Xiaowei & Yan, Zeng Feng, 2017. "Fault detection and diagnosis of chillers using Bayesian network merged distance rejection and multi-source non-sensor information," Applied Energy, Elsevier, vol. 188(C), pages 200-214.
    • Handle: RePEc:eee:appene:v:188:y:2017:i:c:p:200-214
    DOI: 10.1016/j.apenergy.2016.11.130
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    9. Zhao, Yang & Li, Tingting & Zhang, Xuejun & Zhang, Chaobo, 2019. "Artificial intelligence-based fault detection and diagnosis methods for building energy systems: Advantages, challenges and the future," Renewable and Sustainable Energy Reviews, Elsevier, vol. 109(C), pages 85-101.
    10. Chen, Jianli & Zhang, Liang & Li, Yanfei & Shi, Yifu & Gao, Xinghua & Hu, Yuqing, 2022. "A review of computing-based automated fault detection and diagnosis of heating, ventilation and air conditioning systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 161(C).
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    14. Zhu, Xu & Zhang, Shuai & Jin, Xinqiao & Du, Zhimin, 2020. "Deep learning based reference model for operational risk evaluation of screw chillers for energy efficiency," Energy, Elsevier, vol. 213(C).

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