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Fault diagnosis and energy consumption analysis for variable air volume air conditioning system: a case study
Available online at www.sciencedirect.com
ScienceDirect
Available online at www.sciencedirect.com Procedia Engineering 00 (2017) 000–000
ScienceDirect
www.elsevier.com/locate/procedia
Procedia Engineering 205 (2017) 834–841
10th International Symposium on Heating, Ventilation and Air Conditioning, ISHVAC2017, 1922 October 2017, Jinan, China
Fault diagnosis and energy consumption analysis for variable air volume air conditioning system: a case study Yuxiao Lianga, Qinglong Menga,b,c,*, Sainan Changa a a
School of Environmental Science and Engineering, Key Laboratory of Subsurface Hydrology and Ecological Effects in Arid Region, Ministry of Education, Chang’an University, Xi’an, 710054, China b b School of Civil Engineering, Chang’an University, Xi’an, 710061, China ccBRE Trust Centre for Sustainable Engineering, School of Engineering, Cardiff University, Cardiff, CF24 3AA, United Kingdom
Abstract Several common faults and their causes in variable air volume (VAV) air conditioning system are presented, and the principle of fault diagnosis of air conditioning system is briefly described. The VAV air conditioning system was modeled in TRNSYS, and five typical faults of the cooling mode were simulated. The comparative analysis of the respective under normal operation and fault operation had been made, and the impact of each fault on the energy consumption was also analyzed. The actual operating characteristics of air conditioning system was then evaluated. Further, some parameters under fault operations were compared with those under the normal operation, from which the changing characteristics of parameters could be discovered, and the characteristics can be used to diagnose faults. The simulation results demonstrate that fault can affect the energy consumption of VAV air conditioning system, and the impact of each fault is different. In addition, monitoring the change of energy consumption and operation parameters is helpful in fault diagnosis, and the effective fault diagnosis has great significance to energy-saving of the air conditioning system. © 2017 The Authors. Published by Elsevier Ltd. © 2017 The Authors. Published by Ltd. committee of the 10th International Symposium on Heating, Ventilation and Air Peer-review under responsibility of Elsevier the scientific Peer-review under responsibility of the scientific committee of the 10th International Symposium on Heating, Ventilation and Conditioning. Air Conditioning. Keywords: Variable air volume; Air conditioning; Fault diagnosis; Energy consumption; TRNSYS
* *
Corresponding author. Tel.: +86-18229017219 . E-mail address: [email protected]
1877-7058 © 2017 The Authors. Published by Elsevier Ltd. Peer-review under responsibility of the scientific committee of the 10th International Symposium on Heating, Ventilation and Air Conditioning.
1877-7058 © 2017 The Authors. Published by Elsevier Ltd. Peer-review under responsibility of the scientific committee of the 10th International Symposium on Heating, Ventilation and Air Conditioning. 10.1016/j.proeng.2017.10.021
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Yuxiao Liang et al. / Procedia Engineering 205 (2017) 834–841 Yuxiao Liang et al. / Procedia Engineering 00 (2017) 000–000
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1. Introduction The energy consumption of buildings to the total has increased to 30% as the speeding of urbanization process and improvement of people’s lives. The energy consumption of air conditioning system plays an important part in that of the building [1]. The energy saving of air conditioning system is related to the design of the system and its performance in the building. A successful implementation of the fault detection & diagnosis (FDD) can make air conditioning system run according to the optimization program and save 10%-40% of the energy [2]. Variable air volume (VAV) air conditioning system can maintain the indoor thermal comfort in addition to its excellent controllability and a high degree of automation [3]. The complicated structure makes it more difficult to control, and interacting parts of the system usually lead to more faults. The fault of one component will make system compensate the change of room temperature by automating which could affect the performance of other components and cause multiple parameters to change. That is the reason why diagnose fault is full of challenges, in especial how to find out the fault with multiple representative parameters changing at the same time. There are many kinds of faults of air conditioning system which can be sorted into hard fault and soft fault. Hard fault means all the components become invalid completely which usually occurs abruptly but is easy to be detected, like damage of equipment damage and no sensor measurement value. Soft fault means the performance of components decreased which become more and more severe with the increase of time, such as fouling of coils, air leakage and sensor measurement faults. It’s difficult to detect the soft fault, but it can affect the thermal environment and system performance as its long-term existence. FDD is a procedure which can describe the characters of the faults and confirm its range and severity if any fault has been detected [4]. A physical model of each component has been founded separately. The residual between the measured value and the output of the model is compared to the threshold of faults, which is used to diagnose faults. Historical data can be also utilized to found the causal relationship between faults and symptoms which can be used as priori knowledge to diagnose faults by reasoning like qualitative reasoning and fault tree analysis. Scholars have studied the FDD methods: Schein used a set of expert rules to detect faults in air handling units from mass and energy balances [5]. Wang presented an efficient robust fault detection and diagnosis strategy for multiple faults of air handling units by using the residual-based exponentially weighted moving average control chart method, and the validity of this diagnosis strategy is proved [6]. Comstock did experimental studies to simulate the normal operation and fault operation of chillers. The sensitivities of each fault were identified, and the comparison between normal and fault model had been built to generate a symptom matrix that could be used for fault diagnoses [7]. Wang developed a strategy based on a neural network model to diagnose the measurement faults of outdoor air and supply air flow rate sensors. The residuals between the measurement of sensors and the outputs of the model are used to diagnose the faults [8]. Padilla developed a model which can simulate the dynamic behavior of the system under different operating conditions, and based on the use of the data-driven model to detect faults [9]. However, the FDD methods are still limited by the deficiency of sensor data and the dynamic characteristic [10]. Commercialism is not allowed in any manuscripts. Five faults are embedded in the VAV air conditioning system by simulation in the article respectively. According to the analysis of fault characteristics and energy consumption of different faults, operating characteristics under fault operations were evaluated quantitatively and effects of different faults on energy consumption were qualitatively analyzed. 2. Methods Different faults need to be intentionally introduced into the VAV air system in order to obtain specific data for analysis fault characteristic and energy consumption. This plan is difficult to be implemented due to it possible damage to the equipment. Therefore, a simulation method is applied to research in the article, which is more flexible and easier to implement the fault conditions. There are software like ASEAM, TRANSYS, BLAST, TAPP and TAS available for simulation of the entire system. TRANSYS is utilized for research, which is a modularized simulation software and contains many common modules such as varying functions and subprograms. Not only the energy consumption of different kinds of building systems but also the thermal performance and system control can be analyzed.
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An office area of 1270m2 on the full-scale intelligent experimental platform of central air conditioning system of Chang’an University is selected as the building model. An air-cooled chiller is selected in the model. The water system is set as a variable flow primary pump system driven by a frequency converter. Fixed fresh air ratio is set as 0.3. The blend of outdoor fresh air and return air will be processed by air handling unit to the set point before being supplied into the room. The set-point of room temperature is 26.0℃, and the supply air temperature is set as 16.0℃. A PID (proportional-integral-derivative) module is chosen as the controller. The system can control the total air supply rate and the individual air flow rate for each room according to indoor cooling load. The simulation time starts at July 1st (4344h) and ends at July 5th (4464h), and the simulation time is totally 120 hours. The benchmark model of the air conditioning system is shown in Fig. 1. In the simulation, the start and stop time are set as 8:00 and 18:00 respectively with a 0.1h step.
Fig. 1. The structure diagram of VAV air conditioning system.
Hard fault likes fan damage and no value of sensor is direct and easier to be detected as it could cause the system break down, which is difficult to be simulated in TRANSYS. Therefore, five main soft faults which are independent of each other under summer cooling mode are simulated, including positive offset of room temperature sensor (condition 1), negative offset of room temperature sensor (condition 2), positive offset of supply air temperature sensor (condition 3), negative offset of supply air temperature sensor (condition 4), inner surface scaling of the air cooling coils (condition 5). All the fixed parameters and models of equipment under fault conditions are set the same as the normal condition except for the intended faults.
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3. Result and discussion 3.1. Analysis of fault characteristic When VAV air conditioning system under the normal condition, the results of simulations of room temperature (T_room), supply air temperature (T_air), the control signal of supply air flow rate (F_air) and the control signal of chilled water flow rate (F_chw) are shown in Fig. 2(a). The faults of sensors or equipment in control circuit of the VAV air conditioning system will cause its corresponding characterized variables to change, which is defined as the characteristic of the fault. According to the results of the simulation, analysis of the influence of five faults on the system is followed, and the response of the parameters under the different conditions is shown in Fig. 2. a
b
c
d
e
f
Fig. 2. (a) the normal condition; (b) condition 1; (c) condition 2; (d) condition 3; (e) Condition 4; (f) Condition 5.
Yuxiao Liang et al. / Procedia Engineering 205 (2017) 834–841 Yuxiao Liang et al. / Procedia Engineering 00 (2017) 000–000
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In the simulation of condition 1, the measurement of room temperature increases by 2.0℃. The response of the parameters under condition 1 is shown in Fig. 2(b). Condition 1 which means the measured result is higher than the true value, it can impel the controller to send a signal to the terminal air valve requiring for a larger openness so that the supply air flow rate will increase. But the supply air temperature will still increase because the actual cooling capacity cannot meet the demand of supply air flow rate. In order to maintain the supply air temperature, the controller requires a larger openness of chilled water valve in order to help the supply air temperature decrease to the set point, and the reading of room temperature will be stabilized until it attains the set point. However, the cooling capacity much higher than the indoor cooling load, the actual room temperature will be 2.0℃ lower than the set point. In the simulation of condition 2, the measurement of room temperature decreases by 2.0℃. The response of the parameters under condition 2 is shown in Fig. 2(c). Condition 2 which means the measurement is lower than the true value, and the fault characteristics are discussed above change oppositely compared to those under condition 1. It asks for a smaller openness of terminal air valve and chilled water valve. With the cooling capacity much lower than the required cooling load, the actual room temperature will be 2.0℃ higher than the set point. In the simulation of condition 3, the measurement of supply air temperature increases by 1.0℃. The response of the parameters under condition 3 is shown in Fig. 2(d). Condition 3 indicates the measured result is higher than the true value, and it leads the controller to send a signal to the chilled water valve requiring for a larger openness, in order to decrease the supply air temperature to the set point and keep unchanging. However, the actual supply air temperature will higher than its set point, the room temperature will decrease briefly as a result of the decreasing of actual supply air temperature. The terminal air valve is required to decrease the openness in order to maintain the room temperature as the set point. The actual supply air temperature will be 1.0℃ lower than its set point. In the simulation of condition 4, the measurement of supply air temperature decreases by 1.0℃. The response of the parameters under condition 4 is shown in Fig. 2(e). Condition 4 which means the measured result is lower than the true value, and the fault characteristic of condition 3 is the complete opposite of condition 4. It needs a smaller openness of terminal air valve and a larger openness of chilled water valve. The actual supply air temperature will be 1.0℃ higher than the set point eventually. In the simulation of condition 5, heat transfer coefficient of the coil is modulated in order to simulate the effect of scaling. Heat transfer rate between the system and air will decrease which impels the air temperature increase temporarily. The response of the parameters under condition 3 is shown in Fig. 2(f). The chilled water valve is required to increase the openness in order to maintain the supply air temperature as the set point. The room temperature and the control signal of the terminal air valve can maintain stable. The results show that the offset of supply air temperature sensor has the most significant influence on chilled water flow rate, and the offset of room temperature sensor has the most significant influence on supply air flow rate. The scaling on coil inner surface can influence the flow rate of the chilled water. Within a certain range of scaling area, the demand of heat transfer rate can still be met by increasing the openness of chilled water valve. Excessive scaling area will cause room temperature to exceed the set value even with the largest openness of the chilled water valve. The changing trend of different parameters under fault condition against a normal condition can be acquired by the simulation and analysis above. The characteristics of five fault conditions are shown in Table 1. Table 1. Fault characteristics of five conditions.
Condition 1 Condition 2 Condition 3 Condition 4 Condition 5
Actual room temperature low high unchanged unchanged unchanged
Actual supply air temperature unchanged unchanged low high unchanged
Signal of terminal air valve increase decrease decrease increase unchanged
Signal of chilled water valve increase decrease increase decrease increase
A one to one correspondence does exist between the faults and the characteristics according to Table 1. The analysis of fault characteristics and the measurement of the effect caused by different faults are the foundation of the matching model where qualitative relationships are established. The relations between faults and the effect can be used to detect and diagnose the faults during the system monitoring.
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3.2. Analysis of energy consumption In addition to the change of characterized variables under a fault condition, energy consumption of the VAV air conditioning system will also change accordingly. The power of equipment is used as an index to analysis the system energy consumption. Instantaneous operating powers of supply fan, pump and chiller from 4344h to 4464h can be output under a normal condition with the TYPE25 module of TRNSYS. The statistics of energy consumption featuring from 4344h to 4368h is shown in Fig. 3. a
b
c
Fig. 3. The energy consumption of (a) supply fan; (b) pump; (c) chiller.
According to Fig. 3(a), the energy consumption of supply fan has the maximum degree of deviation from the standard condition under condition 1, 2, since the variety of room temperature directly affects the air supply. And the supply air flow rate will increase under condition 1, 4 which prompts the static pressure in the duct to decrease. The rotational speed of supply fan will increase in order to keep the static pressure unchanged which impels the energy consumption of supply fan higher than normal condition. On the contrary, the rotational speed of supply fan
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will decrease under condition 2, 3 which causes the energy consumption of supply fan lower. Condition 5 makes no evident difference to the energy consumption of supply fan. According to Fig. 3(b), the energy consumption of pump has the maximum degree of deviation from the standard condition under condition 3, 4, since the influence of supply air temperature variation on pump energy consumption is the most significant. The flow rate of chilled water will increase under condition 1, 3, 5 due to increase the rotational speed of pump which means the energy consumption of pump is much higher than that under normal condition. The flow rate of chilled water will decrease under condition 2, 4 as a result of the decreasing of pump rotational speed which makes the energy consumption of pump lower. According to Fig. 3(c), the changing trend of energy consumption of chiller is similar to the pump, as the refrigeration capacity of pump is generated by the chiller. The actual refrigeration capacity will increase under condition 1, 3, 5 which means the energy consumption of chiller is much higher. The actual refrigeration capacity will decrease under condition 2, 4 which leads to lower energy consumption of chiller. According to the research, the fault conditions make working parameters deviate from the design value which contribute to the change of thermal comfort and cause more energy consumption. VAV air conditioning system can have much more severe effect during long operation in summer especially when a fault occurs and isn’t uncovered in time. 4. Conclusions The results show that changing trends of characterized variables caused by different faults are different, which can be used to distinguish one fault from another by a one-to-one correspondence. It is an efficient method to optimize the operation. Further, the results show the fault of sensor usually won’t affect the basic function of the air conditioning system which makes it difficult to discover the fault. The inaccuracy of sensor makes the measured value deviate from the true value which can cause system to run out of set condition. The lesser degree of inner surface scaling of the air cooling coils will lead to the increase of chilled water flow rate, but the thermal comfort of the air conditioning system will be seriously affected with the aggravating degree of this fault. Besides, the influence of different faults can be determined by comparing the actual value with the optimized one of the energy consumption of each equipment. The offset of the actual value from the optimized value can be used to determine whether there is any extra energy consumption in the system. Acknowledgement The authors would like to thank the National Natural Science Foundation of China (Grant No. 51208059), and Natural Science Basic Research Plan in Shanxi Province of China (Grant No. 2016JM5076). Meanwhile, Qinglong Meng was supported by a visiting fellowship from the China Scholarship Council. References [1] Y. Jiang. Current building energy consumption in China and effective energy efficiency measures, Heating Ventilating & Air Conditioning. 35 (5) (2005) 30-40. [2] M.A. Piette, S.K. Kinney, P. Haves. Analysis of an information monitoring and diagnostic system to improve building operations, Energy and Buildings. 33 (8) (2001) 783-791． [3] X. Huo. Definition, classification and application of VAV systems, Heating Ventilating & Air Conditioning. 27 (5) (1997) 22-26. [4] Z. Du, X. Jin. Detection and diagnosis for multiple faults in VAV systems, Energy and Buildings. 39 (8) (2007) 923-934. [5] J. Schein, S.T. Bushby, N.S. Castro. A rule-based fault detection method for air handling units, Energy and Buildings. 38 (12) (2006) 1485– 1492. [6] H. Wang, Y. Chen. A robust fault detection and diagnosis strategy for multiple faults of VAV air handling units, Energy and Buildings. 127 (2016) 442-451. [7] M.C. Comstock, J.E. Braun, E.A. Groll. The Sensitivity of chiller performance to common faults, HVAC&R Research. 7 (3) (2001) 263-279. [8] S. Wang, Y. Chen. Fault-tolerant control for outdoor ventilation air flow rate in buildings based on neural network, Building and Environment. 37 (7) (2002) 691-704.
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[9] M. Padilla, D. Choinière. A combined passive-active sensor fault detection and isolation approach for air handling units. Energy and Buildings. 99 (2015) 214-219. [10] S. Katipamula, M.R. Brambley. Methods for Fault detection, diagnostics, and prognostics for building systems-a review: Part I, HVAC＆ R Research. 11 (1) (2005) 3-25.
ScienceDirect
Available online at www.sciencedirect.com Procedia Engineering 00 (2017) 000–000
ScienceDirect
www.elsevier.com/locate/procedia
Procedia Engineering 205 (2017) 834–841
10th International Symposium on Heating, Ventilation and Air Conditioning, ISHVAC2017, 1922 October 2017, Jinan, China
Fault diagnosis and energy consumption analysis for variable air volume air conditioning system: a case study Yuxiao Lianga, Qinglong Menga,b,c,*, Sainan Changa a a
School of Environmental Science and Engineering, Key Laboratory of Subsurface Hydrology and Ecological Effects in Arid Region, Ministry of Education, Chang’an University, Xi’an, 710054, China b b School of Civil Engineering, Chang’an University, Xi’an, 710061, China ccBRE Trust Centre for Sustainable Engineering, School of Engineering, Cardiff University, Cardiff, CF24 3AA, United Kingdom
Abstract Several common faults and their causes in variable air volume (VAV) air conditioning system are presented, and the principle of fault diagnosis of air conditioning system is briefly described. The VAV air conditioning system was modeled in TRNSYS, and five typical faults of the cooling mode were simulated. The comparative analysis of the respective under normal operation and fault operation had been made, and the impact of each fault on the energy consumption was also analyzed. The actual operating characteristics of air conditioning system was then evaluated. Further, some parameters under fault operations were compared with those under the normal operation, from which the changing characteristics of parameters could be discovered, and the characteristics can be used to diagnose faults. The simulation results demonstrate that fault can affect the energy consumption of VAV air conditioning system, and the impact of each fault is different. In addition, monitoring the change of energy consumption and operation parameters is helpful in fault diagnosis, and the effective fault diagnosis has great significance to energy-saving of the air conditioning system. © 2017 The Authors. Published by Elsevier Ltd. © 2017 The Authors. Published by Ltd. committee of the 10th International Symposium on Heating, Ventilation and Air Peer-review under responsibility of Elsevier the scientific Peer-review under responsibility of the scientific committee of the 10th International Symposium on Heating, Ventilation and Conditioning. Air Conditioning. Keywords: Variable air volume; Air conditioning; Fault diagnosis; Energy consumption; TRNSYS
* *
Corresponding author. Tel.: +86-18229017219 . E-mail address: [email protected]
1877-7058 © 2017 The Authors. Published by Elsevier Ltd. Peer-review under responsibility of the scientific committee of the 10th International Symposium on Heating, Ventilation and Air Conditioning.
1877-7058 © 2017 The Authors. Published by Elsevier Ltd. Peer-review under responsibility of the scientific committee of the 10th International Symposium on Heating, Ventilation and Air Conditioning. 10.1016/j.proeng.2017.10.021
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Yuxiao Liang et al. / Procedia Engineering 205 (2017) 834–841 Yuxiao Liang et al. / Procedia Engineering 00 (2017) 000–000
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1. Introduction The energy consumption of buildings to the total has increased to 30% as the speeding of urbanization process and improvement of people’s lives. The energy consumption of air conditioning system plays an important part in that of the building [1]. The energy saving of air conditioning system is related to the design of the system and its performance in the building. A successful implementation of the fault detection & diagnosis (FDD) can make air conditioning system run according to the optimization program and save 10%-40% of the energy [2]. Variable air volume (VAV) air conditioning system can maintain the indoor thermal comfort in addition to its excellent controllability and a high degree of automation [3]. The complicated structure makes it more difficult to control, and interacting parts of the system usually lead to more faults. The fault of one component will make system compensate the change of room temperature by automating which could affect the performance of other components and cause multiple parameters to change. That is the reason why diagnose fault is full of challenges, in especial how to find out the fault with multiple representative parameters changing at the same time. There are many kinds of faults of air conditioning system which can be sorted into hard fault and soft fault. Hard fault means all the components become invalid completely which usually occurs abruptly but is easy to be detected, like damage of equipment damage and no sensor measurement value. Soft fault means the performance of components decreased which become more and more severe with the increase of time, such as fouling of coils, air leakage and sensor measurement faults. It’s difficult to detect the soft fault, but it can affect the thermal environment and system performance as its long-term existence. FDD is a procedure which can describe the characters of the faults and confirm its range and severity if any fault has been detected [4]. A physical model of each component has been founded separately. The residual between the measured value and the output of the model is compared to the threshold of faults, which is used to diagnose faults. Historical data can be also utilized to found the causal relationship between faults and symptoms which can be used as priori knowledge to diagnose faults by reasoning like qualitative reasoning and fault tree analysis. Scholars have studied the FDD methods: Schein used a set of expert rules to detect faults in air handling units from mass and energy balances [5]. Wang presented an efficient robust fault detection and diagnosis strategy for multiple faults of air handling units by using the residual-based exponentially weighted moving average control chart method, and the validity of this diagnosis strategy is proved [6]. Comstock did experimental studies to simulate the normal operation and fault operation of chillers. The sensitivities of each fault were identified, and the comparison between normal and fault model had been built to generate a symptom matrix that could be used for fault diagnoses [7]. Wang developed a strategy based on a neural network model to diagnose the measurement faults of outdoor air and supply air flow rate sensors. The residuals between the measurement of sensors and the outputs of the model are used to diagnose the faults [8]. Padilla developed a model which can simulate the dynamic behavior of the system under different operating conditions, and based on the use of the data-driven model to detect faults [9]. However, the FDD methods are still limited by the deficiency of sensor data and the dynamic characteristic [10]. Commercialism is not allowed in any manuscripts. Five faults are embedded in the VAV air conditioning system by simulation in the article respectively. According to the analysis of fault characteristics and energy consumption of different faults, operating characteristics under fault operations were evaluated quantitatively and effects of different faults on energy consumption were qualitatively analyzed. 2. Methods Different faults need to be intentionally introduced into the VAV air system in order to obtain specific data for analysis fault characteristic and energy consumption. This plan is difficult to be implemented due to it possible damage to the equipment. Therefore, a simulation method is applied to research in the article, which is more flexible and easier to implement the fault conditions. There are software like ASEAM, TRANSYS, BLAST, TAPP and TAS available for simulation of the entire system. TRANSYS is utilized for research, which is a modularized simulation software and contains many common modules such as varying functions and subprograms. Not only the energy consumption of different kinds of building systems but also the thermal performance and system control can be analyzed.
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An office area of 1270m2 on the full-scale intelligent experimental platform of central air conditioning system of Chang’an University is selected as the building model. An air-cooled chiller is selected in the model. The water system is set as a variable flow primary pump system driven by a frequency converter. Fixed fresh air ratio is set as 0.3. The blend of outdoor fresh air and return air will be processed by air handling unit to the set point before being supplied into the room. The set-point of room temperature is 26.0℃, and the supply air temperature is set as 16.0℃. A PID (proportional-integral-derivative) module is chosen as the controller. The system can control the total air supply rate and the individual air flow rate for each room according to indoor cooling load. The simulation time starts at July 1st (4344h) and ends at July 5th (4464h), and the simulation time is totally 120 hours. The benchmark model of the air conditioning system is shown in Fig. 1. In the simulation, the start and stop time are set as 8:00 and 18:00 respectively with a 0.1h step.
Fig. 1. The structure diagram of VAV air conditioning system.
Hard fault likes fan damage and no value of sensor is direct and easier to be detected as it could cause the system break down, which is difficult to be simulated in TRANSYS. Therefore, five main soft faults which are independent of each other under summer cooling mode are simulated, including positive offset of room temperature sensor (condition 1), negative offset of room temperature sensor (condition 2), positive offset of supply air temperature sensor (condition 3), negative offset of supply air temperature sensor (condition 4), inner surface scaling of the air cooling coils (condition 5). All the fixed parameters and models of equipment under fault conditions are set the same as the normal condition except for the intended faults.
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3. Result and discussion 3.1. Analysis of fault characteristic When VAV air conditioning system under the normal condition, the results of simulations of room temperature (T_room), supply air temperature (T_air), the control signal of supply air flow rate (F_air) and the control signal of chilled water flow rate (F_chw) are shown in Fig. 2(a). The faults of sensors or equipment in control circuit of the VAV air conditioning system will cause its corresponding characterized variables to change, which is defined as the characteristic of the fault. According to the results of the simulation, analysis of the influence of five faults on the system is followed, and the response of the parameters under the different conditions is shown in Fig. 2. a
b
c
d
e
f
Fig. 2. (a) the normal condition; (b) condition 1; (c) condition 2; (d) condition 3; (e) Condition 4; (f) Condition 5.
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In the simulation of condition 1, the measurement of room temperature increases by 2.0℃. The response of the parameters under condition 1 is shown in Fig. 2(b). Condition 1 which means the measured result is higher than the true value, it can impel the controller to send a signal to the terminal air valve requiring for a larger openness so that the supply air flow rate will increase. But the supply air temperature will still increase because the actual cooling capacity cannot meet the demand of supply air flow rate. In order to maintain the supply air temperature, the controller requires a larger openness of chilled water valve in order to help the supply air temperature decrease to the set point, and the reading of room temperature will be stabilized until it attains the set point. However, the cooling capacity much higher than the indoor cooling load, the actual room temperature will be 2.0℃ lower than the set point. In the simulation of condition 2, the measurement of room temperature decreases by 2.0℃. The response of the parameters under condition 2 is shown in Fig. 2(c). Condition 2 which means the measurement is lower than the true value, and the fault characteristics are discussed above change oppositely compared to those under condition 1. It asks for a smaller openness of terminal air valve and chilled water valve. With the cooling capacity much lower than the required cooling load, the actual room temperature will be 2.0℃ higher than the set point. In the simulation of condition 3, the measurement of supply air temperature increases by 1.0℃. The response of the parameters under condition 3 is shown in Fig. 2(d). Condition 3 indicates the measured result is higher than the true value, and it leads the controller to send a signal to the chilled water valve requiring for a larger openness, in order to decrease the supply air temperature to the set point and keep unchanging. However, the actual supply air temperature will higher than its set point, the room temperature will decrease briefly as a result of the decreasing of actual supply air temperature. The terminal air valve is required to decrease the openness in order to maintain the room temperature as the set point. The actual supply air temperature will be 1.0℃ lower than its set point. In the simulation of condition 4, the measurement of supply air temperature decreases by 1.0℃. The response of the parameters under condition 4 is shown in Fig. 2(e). Condition 4 which means the measured result is lower than the true value, and the fault characteristic of condition 3 is the complete opposite of condition 4. It needs a smaller openness of terminal air valve and a larger openness of chilled water valve. The actual supply air temperature will be 1.0℃ higher than the set point eventually. In the simulation of condition 5, heat transfer coefficient of the coil is modulated in order to simulate the effect of scaling. Heat transfer rate between the system and air will decrease which impels the air temperature increase temporarily. The response of the parameters under condition 3 is shown in Fig. 2(f). The chilled water valve is required to increase the openness in order to maintain the supply air temperature as the set point. The room temperature and the control signal of the terminal air valve can maintain stable. The results show that the offset of supply air temperature sensor has the most significant influence on chilled water flow rate, and the offset of room temperature sensor has the most significant influence on supply air flow rate. The scaling on coil inner surface can influence the flow rate of the chilled water. Within a certain range of scaling area, the demand of heat transfer rate can still be met by increasing the openness of chilled water valve. Excessive scaling area will cause room temperature to exceed the set value even with the largest openness of the chilled water valve. The changing trend of different parameters under fault condition against a normal condition can be acquired by the simulation and analysis above. The characteristics of five fault conditions are shown in Table 1. Table 1. Fault characteristics of five conditions.
Condition 1 Condition 2 Condition 3 Condition 4 Condition 5
Actual room temperature low high unchanged unchanged unchanged
Actual supply air temperature unchanged unchanged low high unchanged
Signal of terminal air valve increase decrease decrease increase unchanged
Signal of chilled water valve increase decrease increase decrease increase
A one to one correspondence does exist between the faults and the characteristics according to Table 1. The analysis of fault characteristics and the measurement of the effect caused by different faults are the foundation of the matching model where qualitative relationships are established. The relations between faults and the effect can be used to detect and diagnose the faults during the system monitoring.
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3.2. Analysis of energy consumption In addition to the change of characterized variables under a fault condition, energy consumption of the VAV air conditioning system will also change accordingly. The power of equipment is used as an index to analysis the system energy consumption. Instantaneous operating powers of supply fan, pump and chiller from 4344h to 4464h can be output under a normal condition with the TYPE25 module of TRNSYS. The statistics of energy consumption featuring from 4344h to 4368h is shown in Fig. 3. a
b
c
Fig. 3. The energy consumption of (a) supply fan; (b) pump; (c) chiller.
According to Fig. 3(a), the energy consumption of supply fan has the maximum degree of deviation from the standard condition under condition 1, 2, since the variety of room temperature directly affects the air supply. And the supply air flow rate will increase under condition 1, 4 which prompts the static pressure in the duct to decrease. The rotational speed of supply fan will increase in order to keep the static pressure unchanged which impels the energy consumption of supply fan higher than normal condition. On the contrary, the rotational speed of supply fan
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will decrease under condition 2, 3 which causes the energy consumption of supply fan lower. Condition 5 makes no evident difference to the energy consumption of supply fan. According to Fig. 3(b), the energy consumption of pump has the maximum degree of deviation from the standard condition under condition 3, 4, since the influence of supply air temperature variation on pump energy consumption is the most significant. The flow rate of chilled water will increase under condition 1, 3, 5 due to increase the rotational speed of pump which means the energy consumption of pump is much higher than that under normal condition. The flow rate of chilled water will decrease under condition 2, 4 as a result of the decreasing of pump rotational speed which makes the energy consumption of pump lower. According to Fig. 3(c), the changing trend of energy consumption of chiller is similar to the pump, as the refrigeration capacity of pump is generated by the chiller. The actual refrigeration capacity will increase under condition 1, 3, 5 which means the energy consumption of chiller is much higher. The actual refrigeration capacity will decrease under condition 2, 4 which leads to lower energy consumption of chiller. According to the research, the fault conditions make working parameters deviate from the design value which contribute to the change of thermal comfort and cause more energy consumption. VAV air conditioning system can have much more severe effect during long operation in summer especially when a fault occurs and isn’t uncovered in time. 4. Conclusions The results show that changing trends of characterized variables caused by different faults are different, which can be used to distinguish one fault from another by a one-to-one correspondence. It is an efficient method to optimize the operation. Further, the results show the fault of sensor usually won’t affect the basic function of the air conditioning system which makes it difficult to discover the fault. The inaccuracy of sensor makes the measured value deviate from the true value which can cause system to run out of set condition. The lesser degree of inner surface scaling of the air cooling coils will lead to the increase of chilled water flow rate, but the thermal comfort of the air conditioning system will be seriously affected with the aggravating degree of this fault. Besides, the influence of different faults can be determined by comparing the actual value with the optimized one of the energy consumption of each equipment. The offset of the actual value from the optimized value can be used to determine whether there is any extra energy consumption in the system. Acknowledgement The authors would like to thank the National Natural Science Foundation of China (Grant No. 51208059), and Natural Science Basic Research Plan in Shanxi Province of China (Grant No. 2016JM5076). Meanwhile, Qinglong Meng was supported by a visiting fellowship from the China Scholarship Council. References [1] Y. Jiang. Current building energy consumption in China and effective energy efficiency measures, Heating Ventilating & Air Conditioning. 35 (5) (2005) 30-40. [2] M.A. Piette, S.K. Kinney, P. Haves. Analysis of an information monitoring and diagnostic system to improve building operations, Energy and Buildings. 33 (8) (2001) 783-791． [3] X. Huo. Definition, classification and application of VAV systems, Heating Ventilating & Air Conditioning. 27 (5) (1997) 22-26. [4] Z. Du, X. Jin. Detection and diagnosis for multiple faults in VAV systems, Energy and Buildings. 39 (8) (2007) 923-934. [5] J. Schein, S.T. Bushby, N.S. Castro. A rule-based fault detection method for air handling units, Energy and Buildings. 38 (12) (2006) 1485– 1492. [6] H. Wang, Y. Chen. A robust fault detection and diagnosis strategy for multiple faults of VAV air handling units, Energy and Buildings. 127 (2016) 442-451. [7] M.C. Comstock, J.E. Braun, E.A. Groll. The Sensitivity of chiller performance to common faults, HVAC&R Research. 7 (3) (2001) 263-279. [8] S. Wang, Y. Chen. Fault-tolerant control for outdoor ventilation air flow rate in buildings based on neural network, Building and Environment. 37 (7) (2002) 691-704.
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