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Sensor-fault detection, diagnosis and estimation for centrifugal chiller systems using principal-component analysis method

Citations

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Cited by:

  1. Guo, Yabin & Tan, Zehan & Chen, Huanxin & Li, Guannan & Wang, Jiangyu & Huang, Ronggeng & Liu, Jiangyan & Ahmad, Tanveer, 2018. "Deep learning-based fault diagnosis of variable refrigerant flow air-conditioning system for building energy saving," Applied Energy, Elsevier, vol. 225(C), pages 732-745.
  2. Du, Zhimin & Chen, Ling & Jin, Xinqiao, 2017. "Data-driven based reliability evaluation for measurements of sensors in a vapor compression system," Energy, Elsevier, vol. 122(C), pages 237-248.
  3. Icksung Kim & Woohyun Kim, 2021. "Development and Validation of a Data-Driven Fault Detection and Diagnosis System for Chillers Using Machine Learning Algorithms," Energies, MDPI, vol. 14(7), pages 1-24, April.
  4. Li, Tingting & Zhou, Yangze & Zhao, Yang & Zhang, Chaobo & Zhang, Xuejun, 2022. "A hierarchical object oriented Bayesian network-based fault diagnosis method for building energy systems," Applied Energy, Elsevier, vol. 306(PB).
  5. Koo, Jabeom & Yoon, Sungmin, 2022. "In-situ sensor virtualization and calibration in building systems," Applied Energy, Elsevier, vol. 325(C).
  6. Du, Zhimin & Jin, Xinqiao & Yang, Yunyu, 2009. "Fault diagnosis for temperature, flow rate and pressure sensors in VAV systems using wavelet neural network," Applied Energy, Elsevier, vol. 86(9), pages 1624-1631, September.
  7. Rongjiang Ma & Xianlin Wang & Ming Shan & Nanyang Yu & Shen Yang, 2020. "Recognition of Variable-Speed Equipment in an Air-Conditioning System Using Numerical Analysis of Energy-Consumption Data," Energies, MDPI, vol. 13(18), pages 1-14, September.
  8. Yu, Xinran & Ergan, Semiha & Dedemen, Gokmen, 2019. "A data-driven approach to extract operational signatures of HVAC systems and analyze impact on electricity consumption," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
  9. William Nelson & Charles Culp, 2022. "Machine Learning Methods for Automated Fault Detection and Diagnostics in Building Systems—A Review," Energies, MDPI, vol. 15(15), pages 1-20, July.
  10. Thierno M. L. Diallo & Sébastien Henry & Yacine Ouzrout & Abdelaziz Bouras, 2018. "Data-Based Fault Diagnosis Model Using a Bayesian Causal Analysis Framework," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 17(02), pages 583-620, March.
  11. Zhang, Rongpeng & Hong, Tianzhen, 2017. "Modeling of HVAC operational faults in building performance simulation," Applied Energy, Elsevier, vol. 202(C), pages 178-188.
  12. Xiao, Fu & Wang, Shengwei, 2009. "Progress and methodologies of lifecycle commissioning of HVAC systems to enhance building sustainability," Renewable and Sustainable Energy Reviews, Elsevier, vol. 13(5), pages 1144-1149, June.
  13. 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.
  14. Simon P. Melgaard & Kamilla H. Andersen & Anna Marszal-Pomianowska & Rasmus L. Jensen & Per K. Heiselberg, 2022. "Fault Detection and Diagnosis Encyclopedia for Building Systems: A Systematic Review," Energies, MDPI, vol. 15(12), pages 1-50, June.
  15. Wang, Huilong & Xu, Peng & Lu, Xing & Yuan, Dengkuo, 2016. "Methodology of comprehensive building energy performance diagnosis for large commercial buildings at multiple levels," Applied Energy, Elsevier, vol. 169(C), pages 14-27.
  16. 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).
  17. Fan, Cheng & Xiao, Fu & Zhao, Yang & Wang, Jiayuan, 2018. "Analytical investigation of autoencoder-based methods for unsupervised anomaly detection in building energy data," Applied Energy, Elsevier, vol. 211(C), pages 1123-1135.
  18. 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.
  19. Liu, Jiangyan & Li, Guannan & Liu, Bin & Li, Kuining & Chen, Huanxin, 2019. "Knowledge discovery of data-driven-based fault diagnostics for building energy systems: A case study of the building variable refrigerant flow system," Energy, Elsevier, vol. 174(C), pages 873-885.
  20. Zhao, Yang & Wang, Shengwei & Xiao, Fu, 2013. "Pattern recognition-based chillers fault detection method using Support Vector Data Description (SVDD)," Applied Energy, Elsevier, vol. 112(C), pages 1041-1048.
  21. Wen, Shuqing & Zhang, Weirong & Sun, Yifu & Li, Zhenxi & Huang, Boju & Bian, Shouguo & Zhao, Lin & Wang, Yan, 2023. "An enhanced principal component analysis method with Savitzky–Golay filter and clustering algorithm for sensor fault detection and diagnosis," Applied Energy, Elsevier, vol. 337(C).
  22. 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.
  23. Zhuang, Chaoqun & Wang, Shengwei & Shan, Kui, 2020. "A risk-based robust optimal chiller sequencing control strategy for energy-efficient operation considering measurement uncertainties," Applied Energy, Elsevier, vol. 280(C).
  24. Ma, Zhenjun & Wang, Shengwei, 2009. "Building energy research in Hong Kong: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 13(8), pages 1870-1883, October.
  25. Ren, Haoshan & Xu, Chengliang & Lyu, Yuanli & Ma, Zhenjun & Sun, Yongjun, 2023. "A thermodynamic-law-integrated deep learning method for high-dimensional sensor fault detection in diverse complex HVAC systems," Applied Energy, Elsevier, vol. 351(C).
  26. Gao, Dian-ce & Wang, Shengwei & Sun, Yongjun & Xiao, Fu, 2012. "Diagnosis of the low temperature difference syndrome in the chilled water system of a super high-rise building: A case study," Applied Energy, Elsevier, vol. 98(C), pages 597-606.
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