Swire Properties Management Ltd.

Hong Kong, Hong Kong

Swire Properties Management Ltd.

Hong Kong, Hong Kong
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Cheng S.,Hunan University | Cheng S.,Key Laboratory of Building Safety and Energy Efficiency | Chen Y.,Hunan University | Chen Y.,Key Laboratory of Building Safety and Energy Efficiency | And 7 more authors.
Advances in Intelligent and Soft Computing | Year: 2012

The paper demonstrates a robust control strategy for variable air volume (VAV) air handling unit (AHU) systems which are commonly used in office buildings. Compared with traditional VAV AHU control adopting constant the setpoints of static pressure and supply air temperature, the strategy is composed of two independent modules, static pressure reset control and supply air temperature reset control. The optimization is realized by identifying optimal static pressure setpoint and supply air temperature setpoint to achieve predefined thermal comfort standards. The thermal comfort standards can be customized based on the practical operation performance through altering related parameters to tolerate unavoidable defects in real VAV AHU systems. The strategy was developed into software and applied in AHU systems of an office building located in Hong Kong. Its control performance was evaluated with respect to energy saving and indoor thermal comfort maintenance and proved to be effective. © 2012 Springer-Verlag GmbH Berlin Heidelberg.


Wang H.,Hunan University | Wang H.,Henan University of Technology | Wang H.,Key Laboratory of Building Safety and Energy Efficiency | Chen Y.,Hunan University | And 5 more authors.
Energy and Buildings | Year: 2012

An online model-based fault detection and diagnosis (FDD) strategy is presented in this paper to diagnose abrupt faults of variable-air-volume (VAV) air handling units (AHU). The FDD strategy proposed adopts a hybrid approach integrating model-based FDD method and rule-based FDD method. Self-tuning model is used to detect the faults in AHU systems. Model parameters are adjusted by a genetic algorithm-based optimization method to reduce the residual between prediction and measurement. If the residual exceeds the corresponding fault detection threshold, it indicates the occurrence of fault or abnormality in AHU systems. Meanwhile, an online adaptive scheme is proposed to estimate the fault detection threshold, which varies with system operating conditions. Furthermore, three rule-based fault classifiers are developed and utilized to find fault sources. The FDD strategy proposed was tested and validated on real VAV air-conditioning systems involving multiple faults. The validation results show that the FDD strategy proposed can provide an effective tool for detecting and diagnosing the faults of air handling units. © 2012 Elsevier B.V. All rights reserved.


Wang H.,Hunan University | Chen Y.,Hunan University | Wang J.,Hunan University | Chan C.W.H.,Swire Properties Management Ltd | Qin J.,Swire Properties Management Ltd
2011 International Conference on Electric Technology and Civil Engineering, ICETCE 2011 - Proceedings | Year: 2011

Research on fault detection and diagnosis of variable air volume (VAV) air-conditioning systems is of great significance for energy conservation and stable operation of the air-conditioning systems. A fault detection and diagnosis (FDD) strategy using a hybrid method is presented for VAV air handling units in this paper. The Cumulative sum (CUSUM) control charts are used to detect faults in air handling units. The CUSUM control charts are used to monitor the temperature errors between the supply air temperature and its setpoint. Faults are detected when the CUSUM values exceed the chart limits. The fault detection method is robust enough to cope with the non-stationary characteristics of the VAV air handling units. Three rule-based fault classifiers consisting of expert rules and fault isolation algorithms are developed to isolate fault sources. The FDD strategy was tested and validated using in real time data from real VAV air-conditioning systems. © 2011 IEEE.


Wang H.,Hunan University | Wang H.,Key Laboratory of Building Safety and Energy Efficiency | Chen Y.,Hunan University | Chen Y.,Key Laboratory of Building Safety and Energy Efficiency | And 2 more authors.
Automation in Construction | Year: 2012

Proper design, reliable control and health of variable-air-volume (VAV) terminals are essential for reliable operation and energy efficiency of VAV air-conditioning systems. This paper presents an online fault diagnosis tool for pressure-independent VAV terminals and its implementation. The fault diagnosis tool is developed using a robust fault detection and diagnosis strategy for VAV terminals. Cumulative sum (CUSUM) control chart with estimated process parameters is utilized to detect faults in VAV terminals. A rule-based fault classifier is designed and utilized to find the sources of faults in VAV terminals. The fault diagnosis tool relies only upon the sensor data and control signals that are commonly available in building management control systems (BMCS). The fault diagnosis tool can be installed in BMCS to online read the operating data of all VAV terminals in a building through local area network and diagnose faults of VAV terminals in the building. The fault diagnosis tool has been validated in a large-scale office building located in Hong Kong and can effectively find all faults occurring in VAV terminals. © 2011 Elsevier B.V. All rights reserved.


Wang H.,Hunan University | Wang H.,Key Laboratory of Building Safety and Energy Efficiency | Chen Y.,Hunan University | Chen Y.,Key Laboratory of Building Safety and Energy Efficiency | And 2 more authors.
Applied Mechanics and Materials | Year: 2011

The increasing performance demands and the growing complexity of heating, ventilation and air conditioning (HVAC) systems have created a need for automated fault detection and diagnosis (FDD) tools. Cost-effective fault detection and diagnosis method is critical to develop FDD tools. To this end, this paper presents a model-based online fault detection method for air handling units (AHU) of real office buildings. The model parameters are periodically adjusted by a genetic algorithm-based optimization method to reduce the residual between measured and predicted data, so high modeling accuracy is assured. If the residual between measured and estimated performance data exceeds preset thresholds, it means the occurrence of faults or abnormalities in the air handling unit system. in addition, an online adaptive scheme is developed to estimate and update the thresholds, which vary with system operating conditions. The model-based fault detection method needs no additional instrumentation in implementation and can be easily integrated with existing energy management and control systems (EMCS). The fault detection method was tested and validated using in real time data collected from a real office building. © (2011) Trans Tech Publications.


Wang H.,Hunan University | Wang H.,Key Laboratory of Building Safety and Energy Efficiency | Chen Y.,Hunan University | Chen Y.,Key Laboratory of Building Safety and Energy Efficiency | And 2 more authors.
Energy and Buildings | Year: 2011

A robust fault detection and diagnosis (FDD) strategy using a hybrid approach is presented for pressure-independent variable air volume (VAV) terminals in this paper. The residual-based cumulative sum (CUSUM) control charts are utilized to detect faults in VAV terminals. The residuals between the temperature error and its predication are generated using autoregressive time-series models. The standard CUSUM control charts are used to monitor the residuals which are statistically independent. If the CUSUM value exceeds the chart limits, it means the occurrence of fault or abnormity in the corresponding VAV terminal. The residual-based CUSUM control chart can improve the accuracy of fault detection through eliminating the effects of serial correlation on the performance of control charts. Also, the residual-based CUSUM control chart can enhance the robustness and reliability of fault detection through reducing the impacts of normal transient changes. A rule-based fault classifier consisting of expert rules and fault isolation algorithms is developed to isolate 15 fault sources. The FDD strategy was online tested and validated using in real time data collected from real VAV air-conditioning systems. © 2011 Elsevier B.V. All rights reserved.

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