Skip to main content
SpringerLink
Log in

A statistical-based online cross-system fault detection method for building chillers

  • Research Article
  • Advances in Modeling and Simulation Tools
  • Published:
Building Simulation Aims and scope Submit manuscript

Abstract

Practical applications of data-driven fault detection (FD) are limited by their portability. The costs of model training and validation are extremely high when each system requires a model retrained on its own fault and fault-free data. Therefore, this paper proposes a statistical-based online cross-system FD method to address the problem of model portability. The proposed FD model can be cross-utilized between building chillers with various specifications while it only needs to update the original fault detection model by the normal operation data of the new chiller system, thus saving huge fault experimental costs for the fault detection of new chiller. First, a theoretical basis for the proposed cross-system fault detection method is presented. Then, experiments were conducted on three building chillers with different specifications. Both fault and fault-free data were collected from the three chillers. The development and validation of the proposed cross-system fault detection method are then conducted. Results show that the cross-system fault detection models perform well when used with different chillers. For instance, when the fault detection model of system #1 was cross-utilized to system #2, the detection accuracies of refrigerant leakage, refrigerant overcharge, and reduced evaporator water flow were 99.73%, 90.17%, and 96.94%, respectively. Compared with original models, the detection accuracies were improved by 33.78%, 84.07%, and 65.56%, respectively. Therefore, the proposed cross-system fault detection method has potential for online application to practical engineering FD.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  • Barbeito I, Zaragoza S, Tarrío-Saavedra J, et al. (2017). Assessing thermal comfort and energy efficiency in buildings by statistical quality control for autocorrelated data. Applied Energy, 190: 1–17.

    Article  Google Scholar 

  • Bellanco I, Fuentes E, Vallès M, et al. (2021). A review of the fault behavior of heat pumps and measurements, detection and diagnosis methods including virtual sensors. Journal of Building Engineering, 39: 102254.

    Article  Google Scholar 

  • Chen K, Wang Z, Gu X, et al. (2021). Multicondition operation fault detection for chillers based on global density-weighted support vector data description. Applied Soft Computing, 112: 107795.

    Article  Google Scholar 

  • Cheung H, Braun JE (2016). Empirical modeling of the impacts of faults on water-cooled chiller power consumption for use in building simulation programs. Applied Thermal Engineering, 99: 756–764.

    Article  Google Scholar 

  • Fan C, Xiao F, Yan C (2015). A framework for knowledge discovery in massive building automation data and its application in building diagnostics. Automation in Construction, 50: 81–90.

    Article  Google Scholar 

  • Fan C, Yan D, Xiao F, et al. (2021). Advanced data analytics for enhancing building performances: From data-driven to big data-driven approaches. Building Simulation, 14: 3–24.

    Article  Google Scholar 

  • Frank S, Jin X, Studer D, et al. (2018). Assessing barriers and research challenges for automated fault detection and diagnosis technology for small commercial buildings in the United States. Renewable and Sustainable Energy Reviews, 98: 489–499.

    Article  Google Scholar 

  • Harrou F, Nounou MN, Nounou HN, et al. (2015). PLS-based EWMA fault detection strategy for process monitoring. Journal of Loss Prevention in the Process Industries, 36: 108–119.

    Article  Google Scholar 

  • Jia Y, Reddy TA (2003). Characteristic physical parameter approach to modeling chillers suitable for fault detection, diagnosis, and evaluation. Journal of Solar Energy Engineering, 125: 258–265.

    Article  Google Scholar 

  • Kim M, Yoon SH, Domanski PA, et al. (2008). Design of a steady-state detector for fault detection and diagnosis of a residential air conditioner. International Journal of Refrigeration, 31: 790–799.

    Article  Google Scholar 

  • Kim W, Katipamula S (2018). A review of fault detection and diagnostics methods for building systems. Science and Technology for the Built Environment, 24: 3–21.

    Article  Google Scholar 

  • Kim W, Lee JH (2021). Fault detection and diagnostics analysis of air conditioners using virtual sensors. Applied Thermal Engineering, 191: 116848.

    Article  Google Scholar 

  • Li H, Yu D, Braun JE (2011). A review of virtual sensing technology and application in building systems. HVAC&R RESEARCH, 17: 619–645.

    Google Scholar 

  • Li G, Hu Y (2018). Improved sensor fault detection, diagnosis and estimation for screw chillers using density-based clustering and principal component analysis. Energy and Buildings, 173: 502–515.

    Article  Google Scholar 

  • Li Y, O’Neill Z (2018). A critical review of fault modeling of HVAC systems in buildings. Building Simulation, 11: 953–975.

    Article  Google Scholar 

  • Li G, Yao Q, Fan C, et al. (2021a). An explainable one-dimensional convolutional neural networks based fault diagnosis method for building heating, ventilation and air conditioning systems. Building and Environment, 203: 108057.

    Article  Google Scholar 

  • Li T, Zhao Y, Zhang C, et al. (2021b). A knowledge-guided and data-driven method for building HVAC systems fault diagnosis. Building and Environment, 198: 107850.

    Article  Google Scholar 

  • Liu J, Li G, Liu B, et al. (2019). Knowledge discovery of data-drivenbased fault diagnostics for building energy systems: A case study of the building variable refrigerant flow system. Energy, 174: 873–885.

    Article  Google Scholar 

  • Liu J, Li K, Liu B, et al. (2020). Improvement of the energy evaluation methodology of individual office building with dynamic energy grading system. Sustainable Cities and Society, 58: 102133.

    Article  Google Scholar 

  • Liu J, Zhang Q, Li X, et al. (2021). Transfer learning-based strategies for fault diagnosis in building energy systems. Energy and Buildings, 250: 111256.

    Article  Google Scholar 

  • Luo XJ, Fong KF (2020). Novel pattern recognition-enhanced sensor fault detection and diagnosis for chiller plant. Energy and Buildings, 228: 110443.

    Article  Google Scholar 

  • Mao Q, Fang X, Hu Y, et al. (2018). Chiller sensor fault detection based on empirical mode decomposition threshold denoising and principal component analysis. Applied Thermal Engineering, 144: 21–30.

    Article  Google Scholar 

  • Mirnaghi MS, Haghighat F (2020). Fault detection and diagnosis of large-scale HVAC systems in buildings using data-driven methods: A comprehensive review. Energy and Buildings, 229: 110492.

    Article  Google Scholar 

  • Montgomery DC (2019). Introduction to Statistical Quality Control, 8th edn. New York: John Wiley & Sons.

    MATH  Google Scholar 

  • Pérez-Lombard L, Ortiz J, Pout C (2008). A review on buildings energy consumption information. Energy and Buildings, 40: 394–398.

    Article  Google Scholar 

  • Roberts SW (1959). Control chart tests based on geometric moving averages. Technometrics, 1: 239–250.

    Article  Google Scholar 

  • Rogers AP, Guo F, Rasmussen BP (2019). A review of fault detection and diagnosis methods for residential air conditioning systems. Building and Environment, 161: 106236.

    Article  Google Scholar 

  • Shi Z, O’Brien W (2019). Development and implementation of automated fault detection and diagnostics for building systems: A review. Automation in Construction, 104: 215–229.

    Article  Google Scholar 

  • Tran DAT, Chen Y, Ao HL, et al. (2016). An enhanced chiller FDD strategy based on the combination of the LSSVR-DE model and EWMA control charts. International Journal of Refrigeration, 72: 81–96.

    Article  Google Scholar 

  • Wang Z, Wang Z, He S, et al. (2017). Fault detection and diagnosis of chillers using Bayesian network merged distance rejection and multi-source non-sensor information. Applied Energy, 188: 200–214.

    Article  Google Scholar 

  • Wang Z, Wang L, Tan Y, et al. (2021). Fault detection based on Bayesian network and missing data imputation for building energy systems. Applied Thermal Engineering, 182: 116051.

    Article  Google Scholar 

  • Wei X, Xu G, Kusiak A (2014). Modeling and optimization of a chiller plant. Energy, 73: 898–907.

    Article  Google Scholar 

  • Xia Y, Ding Q, Jing N, et al. (2021a). An enhanced fault detection method for centrifugal chillers using kernel density estimation based kernel entropy component analysis. International Journal of Refrigeration, 129: 290–300.

    Article  Google Scholar 

  • Xia Y, Ding Q, Li Z, Jiang A (2021b). Fault detection for centrifugal chillers using a Kernel Entropy Component Analysis (KECA) method. Building Simulation, 14: 53–61.

    Article  Google Scholar 

  • Zhang L, Leach M (2021). Evaluate the impact of sensor accuracy on model performance in data-driven building fault detection and diagnostics using Monte Carlo simulation. Building Simulation, https://doi.org/10.1007/s12273-021-0833-4.

  • Zhao X, Yang M, Li H (2011). Decoupling features for fault detection and diagnosis on centrifugal chillers (1486-RP). HVAC&R Research, 17: 86–106.

    Article  Google Scholar 

  • Zhao Y, Wang S, Xiao F (2013a). Pattern recognition-based chillers fault detection method using Support Vector Data Description (SVDD). Applied Energy, 112: 1041–1048.

    Article  Google Scholar 

  • Zhao Y, Wang S, Xiao F (2013b). A statistical fault detection and diagnosis method for centrifugal chillers based on exponentially-weighted moving average control charts and support vector regression. Applied Thermal Engineering, 51: 560–572.

    Article  Google Scholar 

  • Zhao X, Yang M, Li H (2014). Field implementation and evaluation of a decoupling-based fault detection and diagnostic method for chillers. Energy and Buildings, 72: 419–430.

    Article  Google Scholar 

  • Zhao X (2015). Lab test of three fault detection and diagnostic methods’ capability of diagnosing multiple simultaneous faults in chillers. Energy and Buildings, 94: 43–51.

    Article  Google Scholar 

  • Zhao Y, Li T, Zhang X, et al. (2019). Artificial intelligence-based fault detection and diagnosis methods for building energy systems: Advantages, challenges and the future. Renewable and Sustainable Energy Reviews, 109: 85–101.

    Article  Google Scholar 

  • Zhu X, Zhang S, Jin X, et al. (2020). Deep learning based reference model for operational risk evaluation of screw chillers for energy efficiency. Energy, 213: 118833.

    Article  Google Scholar 

  • Zhu X, Chen K, Anduv B, et al. (2021). Transfer learning based methodology for migration and application of fault detection and diagnosis between building chillers for improving energy efficiency. Building and Environment, 200: 107957.

    Article  Google Scholar 

Download references

Acknowledgements

This work was supported by the Natural Science Foundation of Chongqing (No. cstc2019jcyj-msxmX0537), the China Postdoctoral Science Foundation (No. 2021M693714), the Chongqing Postdoctoral Science Foundation (No. cstc2020jcyj-bshX0073), the National Natural Science Foundation of China (No. 51906181), the Excellent Young and Middle-aged Talent in Universities of Hubei (No. Q20181110) and the Graduate Research and Innovation Foundation of Chongqing (No. CYS20013).

Author information

Authors and Affiliations

  1. Key Laboratory of Low-Grade Energy Utilization Technologies and Systems, Chongqing University, Ministry of Education, Chongqing, China

    Jiangyan Liu, Xin Li, Chuang Wu, DingChao Li, Qing Zhang & Kuining Li

  2. School of Energy and Power Engineering, Chongqing University, Chongqing, China

    Jiangyan Liu, Xin Li, Chuang Wu, DingChao Li, Qing Zhang & Kuining Li

  3. School of Urban Construction, Wuhan University of Science and Technology, Wuhan, China

    Guannan Li

  4. Chongqing Midea General Refrigeration Equipment Co., Ltd, Chongqing, China

    Hailong Lu, Yunqian Zhang & Jinjiang Zhang

Authors
  1. Jiangyan Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Xin Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Guannan Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Chuang Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. DingChao Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Qing Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Kuining Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Hailong Lu
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Yunqian Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Jinjiang Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Guannan Li.

Electronic Supplementary Material

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Liu, J., Li, X., Li, G. et al. A statistical-based online cross-system fault detection method for building chillers. Build. Simul. 15, 1527–1543 (2022). https://doi.org/10.1007/s12273-021-0877-5

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12273-021-0877-5

Keywords

Search

Navigation