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Fault detection and diagnosis for the screw chillers using multi-region XGBoost model

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References

  • Bhardwaj, R., R. Mehta, and P. Ramani. 2020. A Comparative Study of Classification Algorithms for Predicting Liver Disorders. Intelligent Computing Techniques for Smart Energy Systems. Intelligent Computing Techniques for Smart Energy Systems. Springer, Singapore. 753–60.
  • Bonvini, M., M. D. Sohn, J. Granderson, M. Wetter, and M. A. Piette. 2014. Robust on-line fault detection diagnosis for HVAC components based on nonlinear state estimation techniques. Applied Energy 124:156–66. doi:10.1016/j.apenergy.2014.03.009
  • Chakraborty, D., and H. Elzarka. 2019. Early detection of faults in HVAC systems using an XGBoost model with a dynamic threshold. Energy and Buildings 185:326–44. doi:10.1016/j.enbuild.2018.12.032
  • Chen, T. Q., and C. Guestrin. 2016. Xgboost. A scalable tree boosting system. Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining, ACM: 785-794.
  • Cimpoesu, E. M., B. D. Ciubotaru, and D. Stefanoiu. 2013. Fault detection and diagnosis using parameter estimation with recursive least squares. In 19th International Conference on Control Systems and Computer Science :18–23. 2013 IEEE:
  • Du, Z. M., B. Fan, X. Q. Jin, and J. L. Chi. 2014. Fault detection and diagnosis for buildings and HVAC systems using combined neural networks and subtractive clustering analysis. Building and Environment 73:1–11. doi:10.1016/j.buildenv.2013.11.021
  • EMSD 2008. Hong Kong Energy End-use Data. The Energy Efficiency Office, Electrical & Mechanical Services Department, Hong Kong.
  • Fan, Y. Q., X. Y. Cui, H. Han, and H. L. Lu. 2019. Chiller Fault Diagnosis with Field Sensors Using the Technology of Imbalanced Data. Applied Thermal Engineering 159:113933. doi:10.1016/j.applthermaleng.2019.113933
  • Fan, Y. Q., X. Y. Cui, H. Han, and H. L. Lu. 2020. Feasibility and improvement of fault detection and diagnosis based on factory-installed sensors for chillers. Applied Thermal Engineering 164:114506. doi:10.1016/j.applthermaleng.2019.114506
  • Gao, X., and J. Hou. 2016. An improved SVM integrated GS-PCA fault diagnosis approach of Tennessee Eastman process. Neurocomputing 174:906–11. doi:10.1016/j.neucom.2015.10.018
  • Gao, Y. G., S. B. Liu, F. Li, and Z. G. Liu. 2016. Fault detection and diagnosis method for cooling dehumidifier based on LS-SVM NARX model. International Journal of Refrigeration 61:69–81. doi:10.1016/j.ijrefrig.2015.08.020
  • Guo, Y. B., J. Y. Wang, H. X. Chen, G. N. Li, R. G. Huang, Y. Yuan, T. Ahmad, and S. B. Sun. 2019. An expert rule-based fault diagnosis strategy for variable refrigerant flow air conditioning systems. Applied Thermal Engineering 149:1223–35. doi:10.1016/j.applthermaleng.2018.12.132
  • Habib, U., K. Hayat, and G. Zucker. 2016. Complex building’s energy system operation patterns analysis using bag of words representation with hierarchical clustering. Complex Adaptive Systems Modeling 4 (1):8. doi:10.1186/s40294-016-0020-0
  • Han, H.,. X. Y. Cui, Y. Q. Fan, and H. Qing. 2019. Least squares support vector machine (LS-SVM)-based chiller fault diagnosis using fault indicative features. Applied Thermal Engineering 154:540–7. doi:10.1016/j.applthermaleng.2019.03.111
  • Han, H.,. B. Gu, T. Wang, and Z. R. Li. 2011. Important sensors for chiller fault detection and diagnosis (FDD) from the perspective of feature selection and machine learning. International Journal of Refrigeration 34 (2):586–99. doi:10.1016/j.ijrefrig.2010.08.011
  • He, S. W., Z. W. Wang, Z. W. Wang, X. W. Gu, and Z. F. Yan. 2016. Fault detection and diagnosis of chiller using Bayesian network classifier with probabilistic boundary. Applied Thermal Engineering 107:37–47. doi:10.1016/j.applthermaleng.2016.06.153
  • House, J. M., H. Vaezi-Nejad, and J. M. Whitcomb. 2001. An expert rule set for fault detection in air-handling units. Ashrae Transactions 107:858–871.
  • Huang, Z. J., Z. S. Wang, and H. G. Zhang. 2017. Multilevel feature moving average ratio method for fault diagnosis of the microgrid inverter switch. IEEE/CAA Journal of Automatica Sinica 4 (2):177–85. doi:10.1109/JAS.2017.7510496
  • Kumar, M., and I. N. Kar. 2009. Non-linear HVAC computations using least square support vector machines. Energy Conversion and Management 50 (6):1411–8. doi:10.1016/j.enconman.2009.03.009
  • Lee, W. Y., J. M. House, and N. H. Kyong. 2004. Subsystem level fault diagnosis of a building's air-handling unit using general regression neural networks. Applied Energy 77 (2):153–70. doi:10.1016/S0306-2619(03)00107-7
  • Lei, Y., W. Jiang, A. Jiang, Y. Zhu, H. Niu, and S. Zhang. 2019. Fault Diagnosis Method for Hydraulic Directional Valves Integrating PCA and XGBoost. Processes 7 (9):589. doi:10.3390/pr7090589
  • Li, G. N., Y. P. Hu, H. X. Chen, H. R. Li, M. Hu, Y. B. Guo, J. Y. Liu, S. B. Sun, and M. Sun. 2017. Data partitioning and association mining for identifying VRF energy consumption patterns under various part loads and refrigerant charge conditions. Applied Energy 185:846–61. doi:10.1016/j.apenergy.2016.10.091
  • Li, Y., and Z. O’Neill. 2018. A critical review of fault modeling of HVAC systems in buildings. Building Simulation 11 (5):953–75. doi:10.1007/s12273-018-0458-4
  • Li, S., and J. Wen. 2010. Development and Validation of a Dynamic Air Handling Unit Model, Part I. ASHRAE Transactions 116 (1):45–56.
  • Li, S., and J. Wen. 2014. A model-based fault detection and diagnostic methodology based on PCA method and wavelet transform. Energy and Buildings 68:63–71. doi:10.1016/j.enbuild.2013.08.044
  • Li, S., J. Wen, X. Zhou, and C. J. Klaassen. 2010. Development and validation of a dynamic air handling unit model, Part 2. ASHRAE Transactions 116 (1):57–73.
  • Mulumba, T., A. Afshari, K. Yan, W. Shen, and L. K. Norford. 2015. Robust model-based fault diagnosis for air handling units. Energy and Buildings 86:698–707. doi:10.1016/j.enbuild.2014.10.069
  • Padilla, M., D. Choinière, and J. A. Candanedo. 2015. A model-based strategy for self-correction of sensor faults in variable air volume air handling units. Science and Technology for the Built Environment 21 (7):1018–32. doi:10.1080/23744731.2015.1025682
  • Shi, S. B., G. N. Li, H. X. Chen, J. Y. Liu, Y. P. Hu, L. Xing, and W. J. Hu. 2017. Refrigerant charge fault diagnosis in the VRF system using Bayesian artificial neural network combined with ReliefF filter. Applied Thermal Engineering 112:698–706. doi:10.1016/j.applthermaleng.2016.10.043
  • Sun, S. B., G. N. Li, H. X. Chen, Q. Y. Huang, S. B. Shi, and W. J. Hu. 2017. A hybrid ICA-BPNN-based FDD strategy for refrigerant charge faults in variable refrigerant flow system. Applied Thermal Engineering 127:718–28. doi:10.1016/j.applthermaleng.2017.08.047
  • Tran, D. A. T., Y. M. Chen, H. L. Ao, and N. T. C. Huong. 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. doi:10.1016/j.ijrefrig.2016.07.024
  • Tran, D. A. T., Y. M. Chen, and C. L. Jiang. 2016. Comparative investigations on reference models for fault detection and diagnosis in centrifugal chiller systems. Energy and Buildings 133:246–56. doi:10.1016/j.enbuild.2016.09.062
  • Tudoroiu, N., M. Zaheeruddin, E. R. Tudoroiu, and V. Jeflea. 2008. Fault detection and diagnosis (FDD) in heating ventilation air conditioning systems (HVAC) using an interactive multiple model augmented unscented Kalman filter (IMMAUKF). 2008 Conference on Human System Interactions IEEE:334–9.
  • Wang, H. T., Y. M. Chen, C. W. H. Chan, J. Y. Qin, and J. H. Wang. 2012. Online model-based fault detection and diagnosis strategy for VAV air handling units. Energy and Buildings 55:252–63. doi:10.1016/j.enbuild.2012.08.016
  • Wang, L. P., and P. Haves. 2014. Monte Carlo analysis of the effect of uncertainties on model-based HVAC fault detection and diagnostics. HVAC&R Research 20 (6):616–27. doi:10.1080/10789669.2014.924354
  • Wang, Z. W., L. Wang, K. F. Liang, and Y. Y. Tan. 2018. Enhanced chiller fault detection using Bayesian network and principal component analysis. Applied Thermal Engineering 141:898–905. doi:10.1016/j.applthermaleng.2018.06.037
  • Xiao, F., S. W. Wang, X. H. Xu, and G. M. Ge. 2009. An isolation enhanced PCA method with expert-based multivariate decoupling for sensor FDD in air-conditioning systems. Applied Thermal Engineering 29 (4):712–22. doi:10.1016/j.applthermaleng.2008.03.046
  • Xiao, F., C. Y. Zheng, and S. W. Wang. 2011. A fault detection and diagnosis strategy with enhanced sensitivity for centrifugal chillers. Applied Thermal Engineering 31 (17-18):3963–70. doi:10.1016/j.applthermaleng.2011.07.047
  • Yang, X. B., X. Q. Jin, Z. M. Du, and Y. H. Zhu. 2011. A novel model-based fault detection method for temperature sensor using fractal correlation dimension. Building and Environment 46 (4):970–9. doi:10.1016/j.buildenv.2010.10.030
  • Yan, R., Z. J. Ma, Y. Zhao, and G. Kokogiannakis. 2016. A decision tree based data-driven diagnostic strategy for air handling units. Energy and Buildings 133:37–45. doi:10.1016/j.enbuild.2016.09.039
  • Yan, K., W. Shen, T. Mulumba, and A. Afshari. 2014. ARX model based fault detection and diagnosis for chillers using support vector machines. Energy and Buildings 81:287–95. doi:10.1016/j.enbuild.2014.05.049
  • Zhao, X. Z., M. Yang, and H. R. Li. 2012. A virtual condenser fouling sensor for chillers. Energy and Buildings 52:68–76. doi:10.1016/j.enbuild.2012.05.018
  • Zhu, X., Z. M. Du, Z. J. Chen, X. Q. Jin, and X. Q. Huang. 2019. Hybrid model based refrigerant charge fault estimation for the data centre air conditioning system. International Journal of Refrigeration 106:392–406. doi:10.1016/j.ijrefrig.2019.07.021
  • Zhu, X., Z. M. Du, X. Q. Jin, and Z. J. Chen. 2019. Fault diagnosis based operation risk evaluation for air conditioning systems in data centers. Building and Environment 163:106319. doi:10.1016/j.buildenv.2019.106319

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