A robust fault detection and diagnosis strategy for pressure-independent VAV terminals of real office buildings

Energy and Buildings 43 (2011) 1774–1783

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A robust fault detection and diagnosis strategy for pressure-independent VAV terminals of real office buildings Haitao Wang a,c , Youming Chen a,c,∗ , Cary W.H. Chan b , Jianying Qin b a

College of Civil Engineering, Hunan University, Changsha, Hunan 410082, China Swire Properties Management Ltd, Island East, Hong Kong, China c Key Laboratory of Building Safety and Energy Efficiency, MOE, China b

a r t i c l e

i n f o

Article history: Received 19 October 2010 Received in revised form 23 February 2011 Accepted 20 March 2011 Keywords: VAV terminal Fault detection Diagnosis CUSUM control chart Expert rules

a b s t r a c t A robust fault detection and diagnosis (FDD) strategy using a hybrid approach is presented for pressureindependent 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.

1. Introduction Variable air volume air-conditioning systems are widely developed and applied in office and commercial buildings to save more energy. VAV air-conditioning systems and their control strategies become more and more complex to meet the increasing demands on indoor environment quality and energy conservation. The complex VAV air-conditioning systems tend to fail to satisfy the performance expectations envisioned at design because of the problems caused by improper design, improper installation, inadequate maintenance, equipment failure, or control and sensor failure. If these faults cannot be detected, diagnosed and removed, they will bring about abnormal operations, which subsequently increase energy consumption of the airconditioning system, deteriorate the indoor air environment and decrease the useful service life of air-conditioning equipment. Fault detection and diagnosis for VAV air-conditioning systems is an important technique to reduce energy consumption, to reduce maintenance costs and to improve comfort [1]. Therefore, it is significant to develop suitable fault detection and

∗ Corresponding author at: College of Civil Engineering, Hunan University, Changsha, Hunan 410082, China. Tel.: +86 731 88823515; fax: +86 731 88823515. E-mail addresses: [email protected], [email protected] (Y. Chen). 0378-7788/$ – see front matter © 2011 Elsevier B.V. All rights reserved. doi:10.1016/j.enbuild.2011.03.018

diagnosis methods that can be used in the VAV air-conditioning systems. In recent years, many fault detection and diagnosis methods have been proposed for VAV air-conditioning systems. Schein et al. [2] used a set of expert rules derived from mass and energy to detect faults in the air handling units (AHU). Qin and Wang [3] presented an automatic fault detection and diagnosis strategy for VAV air-conditioning systems. The FDD strategy utilized a hybrid method consisting of expert rules, performance indexes and statistical process control models to detect and diagnose the faults in pressure-independent VAV terminals. Xiao and Wang [4] developed a robust sensor fault detection and diagnosis strategy based on the PCA method for air handling units. Sensor faults are detected using the Q-statistic and diagnosed using an isolation-enhanced PCA method that combines the Q-contribution plot and knowledgebased analysis. Schein et al. [5] used a small number of control charts to assess the performance of VAV terminals. Most recent studies of fault detection and diagnosis in VAV air-conditioning systems have focused on the major equipments such as chiller, air handling unit, fan, etc. However, study on fault detection and diagnosis of VAV terminals has been relatively less than other types of VAV air-conditioning equipments [6]. VAV terminals are common and key components in VAV airconditioning systems. As VAV terminals serve the end users, their performances have significant effects on the environmental quality provided by heating, ventilation and air conditioning (HVAC) sys-

H. Wang et al. / Energy and Buildings 43 (2011) 1774–1783

Nomenclature ET FD,max FD,min Fmax Fmin Fi Fset hN hS k Ni P Pset Si T Tsa Tsa,set Tset xi yi

temperature error between the measured zone temperature and its setpoint (◦ C) manufacturer’s scheduled maximum air flow rate setpoint of VAV terminal (l/s) manufacturer’s scheduled minimum air flow rate setpoint of VAV terminal (l/s) maximum air flow rate setpoint of VAV terminal (l/s) minimum air flow rate setpoint of VAV terminal (l/s) measured air flow rate through VAV terminal at sampling time i (l/s) air flow rate setpoint of VAV terminal (l/s) lower chart limit upper chart limit slack parameter cumulative sum for negative deviations at sampling time i supply air static pressure (Pa) supply air static pressure setpoint (Pa) cumulative sum for positive deviations at sampling time i measured zone temperature (◦ C) supply air temperature of AHU (◦ C) supply air temperature setpoint of AHU (◦ C) zone temperature setpoint (◦ C) process error between a process variable and its setpoint at sampling time i normalized error between a process variable and its setpoint at sampling time i

Greek symbols ˛ weighting factor εf error threshold for air flow rate εp error threshold for supply air static pressure εsa error threshold for supply air temperature of AHU error threshold for zone temperature εt  parameter for autoregressive process  control signal of terminal damper opening (0–100%) max maximum control signal of terminal damper opening (0–100%) min minimum control signal of terminal damper opening (0–100%)  ˆ0 estimate of the in-control process mean estimate of the in-control process standard deviaˆ 0 tion ω ˆ residual between the temperature error and its prediction

tems and the energy efficiency of buildings. There are some barriers to detect and diagnose faults in VAV terminals. First, VAV terminals are instrumented with the minimum number of sensors sufficient to implement basic local-loop and supervisory control strategies, lack of sensor information is a significant barrier to detect and diagnose faults in VAV terminals. Second, there is little effort to consolidate the information into a clear and coherent picture of VAV terminals status. The data that is collected overwhelms building operators. Third, building operators may not fully understand the control strategies implemented, they may overlook symptoms of a failure [7]. Fourth, the quantity of VAV terminals in a VAV system is huge, their disparate locations in false ceiling areas, they benefit from almost no preventive maintenance. As a result, study on fault detection and diagnosis for the VAV terminals is very insufficient.

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A robust fault detection and diagnosis strategy using a hybrid method is presented for pressure-independent VAV terminals in this paper. The residual-based CUSUM control charts [8] are utilized to detect faults. A rule-based fault classifier is developed to find fault sources. The fault detection and diagnosis strategy is tested and evaluated using in real time data collected from real VAV air-conditioning systems. The FDD strategy can be conveniently implemented on real buildings as it relies only upon sensor data, design data, and control signals that are commonly available in energy management and control system (EMCS). 2. Building description and faults in VAV terminals 2.1. Building and air-conditioning system description The fault detection and diagnosis strategy developed was tested and validated on VAV air-conditioning systems in a 36-story office building located in Hong Kong. The building is 166.5 m high. Each floor of the building has an area of 786 m2 . The 2nd, 20th and roof floors service as mechanical floors. Each floor in the building is served by a single duct VAV air-conditioning system. Fresh air is transported to every floor by fresh air fan. Return air is sent to the AHU through the ceilings. Supply air is handled to the supply air temperature setpoint at the AHU. The supply air pressure would be maintained at its setpoint through regulating the supply air fan speed. Pressure-independent VAV terminals maintain zone temperature at its setpoint through regulating supply air volume into the rooms. Totally, there are 1186 pressure-independent VAV terminals installed in the office building. Fig. 1 shows the schematic of pressure-independent VAV terminal and control instrumentation used in the office building. The pressure-independent VAV terminal uses two cascading control loops, the first loop controls zone temperature; its output is an air flow rate setpoint that is limited to a range between the minimum air flow rate setpoint and the maximum air flow rate setpoint. This air flow rate setpoint is then sent to the second control loop, which modulates the VAV damper to maintain the air flow rate at its setpoint [9]. Because Hong Kong is located in a typical subtropical region, its buildings are subjected to high cooling demands for air-conditioning systems throughout most of year [10]. Pressure-independent VAV terminals in the office building do not have reheating capability. 2.2. Operational and design data requirements A fully automated energy management and control system is installed in the office building, the huge amount of data available in EMCS central stations and digital outstations provide rich information for monitoring, optimization and diagnosis of the VAV air-conditioning systems. The EMCS can monitor and gather the operation data of all AHU systems and VAV terminals, and the operation data is stored in a structured query language (SQL) server, which is a collection of data items organised as a set of tables from which data can be accessed or reassembled in many different ways without having to reorganise the database tables. The fault detection and diagnosis strategy uses sensor data, control signals, and design data as follows. • • • • • • • •

Control signal of VAV terminal damper opening (%); measured zone air flow rate (l/s); zone air flow rate setpoint (l/s); minimum air flow rate setpoint (l/s); maximum air flow rate setpoint (l/s); measured zone temperature (◦ C); zone temperature setpoint (◦ C); measured supply air duct statistic pressure (Pa);

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Supply air

Damper

Total Pressure Sensor

Zone Temperature Sensor

P

P

Supply air to room

T

Static Pressure Sensor Damper Motor

DM

DPT

Proportional-Integral-Derivative Controller Differential Pressure Transmitter

PID Controller Fig. 1. Schematic of pressure-independent VAV terminal and control instrumentation.

• supply air duct statistic pressure setpoint (Pa); • manufacturer’s scheduled maximum air flow rate of VAV terminal (l/s); • manufacturer’s scheduled minimum air flow rate of VAV terminal (l/s); • supply air temperature of AHU (◦ C); • supply air temperature setpoint of AHU (◦ C); • supply air fan status signal (1 is on, 0 is off). 2.3. Faults in VAV terminals In order to obtain the fault information of VAV terminals in the office building, half-year operation data logged at 5 min intervals from the SQL server was offline analyzed and offline diagnosed. An on-site survey was carried out on the abnormal VAV terminals according to the results of offline analysis and fault diagnosis. The consequences of analysis, diagnosis and survey showed that very often the measured air flow rate could not approach its setpoint and the zone temperature could not approach its setpoint. These consequences match with the conclusion of the survey on airconditioning system faults conducted by Yoshida et al. [11], which covered a wider range of faults from design faults to user-level faults in VAV air-conditioning systems. Concerning VAV terminals, their survey revealed that zone air temperature deviation and local direct digital control error were common. Fault types found in the pressure-independent VAV terminals of the office building are summarized in Table 1.

3. Fault detection and diagnosis strategy for VAV terminals The fault detection and diagnosis strategy of pressureindependent VAV terminals uses a hybrid method. The residualbased CUSUM control charts are used to detect faults in pressure-independent VAV terminals. A rule-based fault classifier consisting of expert rules and fault isolation algorithms is developed to isolate fault sources. Fig. 2 shows the flow chart of fault detection and diagnosis for pressure-independent VAV terminals.

3.1. Fault detection 3.1.1. Temperature errors Common equipment failures, control and sensor faults, or improper designs in pressure-independent VAV terminals will result in a positive or negative deviation of the temperature error from its value during normal operation. The temperature error, ET , is defined as Eq. (1). ET = T − Tset

(1)

where T is the zone temperature, Tset is the zone temperature setpoint. Observations collected using a short sampling interval in industrial practice in continuous as well as discrete processes are actually serially correlated [12,13], and the temperature errors at 5 min sampling interval can be proven to be highly correlated by calculating the autocorrelation function as Eq. (2). rk =

n−k (z − z¯ )(zt−k − z¯ ) t=1 t n 2 t=1

(2)

(zt − z¯ )

Table 1 Fault types of VAV terminals. Faults no.

Fault description

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

Air flow rate sensor reading error Damper failure Flow sensor reading deviation to maximum Flow sensor reading deviation to minimum Low supply air static pressure Too big cooling load Too high minimum air flow rate setpoint Too high supply air temperature of AHU Too high temperature setpoint Too low maximum air flow rate setpoint Too low supply air temperature of AHU Too low zone temperature setpoint Too small cooling load VAV controller hard failure VAV terminal under size

H. Wang et al. / Energy and Buildings 43 (2011) 1774–1783

1777

The historical, fault-free operating data

Read current data from SQL server

Calculating temperature error between the zone temperature and its setpoint

Calculating temperature error between the zone temperature and its setpoint

Generating the residual between temperature error and its predication of an autoregressive model

Generating the residual between temperature error and its predication of an autoregressive model

Estimating the in-control mean and standard deviation of the residuals

Applying the standardized CUSUM control charts to the residuasl Y

Si > hS N

Ni > h N A set of expert rules

N

Y A rule-based fault classifier

Fault isolation algorithms List the fault

Fig. 2. Flow chart of fault detection and diagnosis for pressure-independent VAV terminals.

n

where rk is the autocorrelation coefficient at lag k, z¯ = z is t=1 t overall mean, zt is the value of measurement at sampling time t.

The residuals (ω) ˆ between the temperature error and its prediction of the first order autoregressive models are calculated by Eq. (4).

3.1.2. The residual-based CUSUM control charts The serial correlation can have very serious effects on the performance of statistical process control (SPC) charts developed under the independence assumption. In the presence of correlated data, the SPC charts may be rendered ineffective due to excessive false alarm rates [14–18]. Various SPC control charts have been proposed to deal with correlated data recently. The residual-based control chart is one of the most widely investigated methods. The key idea of the residual-based control charts is to fit a time series model to subtract the autocorrelation. Assuming that the time series model is accurate, the residuals between the observation and its prediction of the time series model are statistically independent [19–23]. And then traditional SPC control charts can be applied to the residuals. In this study, the standard CUSUM charts are used to monitor the residuals between the temperature error and its prediction of the first order autoregressive (AR (1)) model. It is interesting to note that CUSUM chart and exponentially weighted moving average (EWMA) chart are equivalent in detecting small changes in the control processes. However, the EWMA charts are not investigated as part of this study. 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 charts can enhance the robustness and reliability of fault detection through reducing the impacts of the normal transient changes, such as startup, normal change in the operating point, abrupt change of cooling load, etc. AR (1) model which is one of the most popular models in the modern time series analysis is used to forecast the temperature error. The predictions of the temperature error using AR (1) models can be written as Eq. (3).

ω ˆ i = Ei − 1 − (Ei−1 − 1 )

Eˆ i = 1 + (Ei−1 − 1 )

(3)

where Eˆ i is the prediction of the temperature error at time i, Ei−1 is the temperature error at time i − 1, 1 is the mean of the temperature errors,  is the parameter for autoregressive process.

(4)

In order to use the standardized CUSUM charts, the residuals between the temperature error and its prediction are standardized by Eq. (5). yi =

ˆ 0) (ω ˆi − ˆ 0

(5)

where yi is the standardized residual at time i,  ˆ 0 is an estimate of in-control process mean of the residuals, ˆ 0 is an estimate of in-control process standard deviation of the residuals. The standardized CUSUM chart has some advantages. Many CUSUM charts can have the same values of the slack parameter and the chart limits, and the choices of these parameters are independent of units of the control variables [24]. Two cumulative sums developed by kemp [25] are used to monitor the residuals between the temperature error and its prediction of AR (1) model. The two cumulative sum control charts are defined by Eqs. (6) and (7). Si = max[0, Si−1 + yi − k]

(6)

Ni = max[0, Ni−1 − yi − k]

(7)

where yi is the standardized residual between the temperature error and its prediction, k is the slack parameter, Si is cumulative sum for positive deviations at sampling time i, S0 = 0, Ni is cumulative sum for negative deviations at sampling time i, N0 = 0. Both S and N are nonnegative. S is used to accumulate positive temperature error deviations. N is utilized to accumulate negative temperature error deviations. If y is greater than k, S will develop an upward trend; likewise, if y is smaller than −k, N will show an upward trend. When S is greater than the chart limit defined by the parameter hs or N is greater than the chart limit defined by the parameter hN , it means an occurrence of fault or abnormity in the corresponding VAV terminal. The CUSUM value for each terminal is calculated when the air-conditioning systems are operating.

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The CUSUM value will be reset to zero when the air-conditioning systems are shut down.

Table 2 Expert rule sets. Rule no.

3.1.3. Parameter estimation method When the CUSUM control charts are applied to monitor each practical control process, the parameters of the control process are rarely known. These parameters are frequently replaced with the estimates from fault-free reference samples. And then the CUSUM control chart with estimated process parameters can be used to monitor the control process [26]. Error is defined as the difference between the measured control variable and its setpoint. Both the in-control process mean of the errors (0 ) and the in-control process standard deviation of the errors ( 0 ) are unknown. Suppose there are m groups historical errors collected from fault-free process, Xi1 , . . ., Xin are the historical errors of the ith group. The process parameters, 0 and  0 , can be estimated using classical estimation method as follows. 1 Xi,j n n

X¯ i =

(8)

j=1

1  Xij mn m

 ˆ0 =

n

(9)

i=1 j=1

ˆ 0 =



m(n − 1)

 m(n − 1) 



2

m n

(1/2m(n − 1))

i=1

j=1

(Xij − X¯ i )

 ((m(n − 1) + 1)/2)

2

(10)

where Xij is the historical error from fault-free control process, X¯ i is the mean value of the ith group historical errors,  ˆ 0 is an estimate of in-control process mean of the errors, ˆ 0 is an estimate of in-control process standard deviation of the errors,  () is Gamma function. Gamma function is defined as Eq. (11).



 (s) =

+∞

xs−1 e−x dx

(s > 0)

(11)

0

In this study, the in-control process mean and the in-control process standard deviation of the residuals between the temperature error and its prediction were estimated from historical, fault-free operating data. 3.2. Fault diagnosis Rule based FDD methods employing expert rules are used extensively in HVAC applications. The rule based fault detection and diagnosis methods are attractive when the patterns representative of a particular class of operation can be easily identified [27]. A rule-based fault classifier consisting of expert rules and fault isolation algorithms is developed to diagnose fifteen fault sources in this study. 3.2.1. Expert rules for fault diagnosis Twenty expert rules are selected to diagnose fifteen faults in pressure-independent VAV terminals. Any fault that causes a rule to be satisfied would be isolated. Twenty expert rules are shown in Table 2. The zone temperature error threshold (εT ) is set at 1.5 ◦ C. The air flow rate error threshold (εf ) is set at 10 l/s. The supply air duct pressure error threshold (εp ) is set at 30 Pa. The AHU supply air temperature error threshold (εsa ) is set at 1 ◦ C. When the residualbased CUSUM control charts detect faults in pressure-independent VAV terminals, these expert rules will be used to find fault sources.

Fault no.

Rule expression

1

6, 15

2

10

3

14

4 5

2 3

6

13

7

7

8

14

9

1

10

4

11 12 13 14 15

2 8 11 5 14

16

14

17 18 19

14 14 9

20

12

T − Tset > εt and |F − Fset | < εf and Fset = Fmax and Fmax = FD,max T − Tset > εt and |F − Fset | < εf and Fset = Fmax and |Fmax − FD,max | > εf T − Tset > εt and |F − Fset | < εf and |Fset − Fmax | > εf and |Fset − Fmin | > εf T − Tset > εt and  = max and Fset − F > 3εf T − Tset > εt and  = min and Fset = Fmax and F ≥ Fmax Tset − T > εt and |F − Fset | < εf and Fset = Fmin and Fmin = FD,min Tset − T > εt and |F − Fset | < εf and Fset = Fmin and |Fmin − FD,min | > εf Tset − T > εt and |F − Fset | < εf and |Fset − Fmax | > εf and |Fset − Fmin | > εf Fi > εf (i = 1, 2, . . ., M), M is the number of samples in one day Tset − T > εt and  = max and Fset = Fmin and F ≤ Fmin  = 0 and F − Fset > 3εf and Fset = Fmin Tsa − Tsa,set > εsa Tsa − Tsa,set < − εsa P − Pset < − εp T − Tset > εt and Fset = Fmax and  = min and F < Fmax Tset − T > εt and Fset = Fmin and  = max and F > Fmin T − Tset > εt and Fset = Fmin and  = min Tset − T > εt and Fset = Fmax and  = max Tset ≥ h1 , h1 is determined according to specific condition Tset ≤ h2 , h2 is determined according to specific condition

3.2.2. Fault isolation algorithms The VAV terminal has two abnormal operating modes. When the zone temperature is higher than its setpoint, the operating mode of VAV terminal is mode 1. When the zone temperature is lower than its setpoint, the operating mode of VAV terminal is mode 2. Fault isolation algorithms for the two modes are developed, respectively. Once the residual-based CUSUM control charts detected a fault in the pressure-independent VAV terminal, the fault isolation algorithms will initiate fault diagnosis by using corresponding expert rules. (1) Fault isolation algorithm for mode 1 Fault isolation algorithm for mode 1 is shown in Fig. 3. The fault isolation algorithm first compares the measured zone air flow rate with its setpoint. When the measured zone air flow rate can approach its setpoint well, the air flow rate setpoint will be compared with the minimum air flow rate setpoint and the maximum air flow rate setpoint. If the air flow rate setpoint is higher than the minimum air flow rate setpoint and lower than the maximum air flow rate setpoint, VAV controller hard failure can be diagnosed by rule 3. When the air flow rate setpoint is at the maximum, the possible causes of the fault, including too low maximum air flow rate setpoint, too low zone temperature setpoint, and too big cooling load (or VAV terminal under size) can be diagnosed by rule 2, rule 20, and rule 1, respectively. If the measured zone air flow rate does not approach its setpoint, the possible causes of the fault, including damper failure, flow sensor reading deviation to maximum, air flow rate sensor reading error, too high supply air temperature of AHU, low supply air static pressure, and VAV controller hard failure can be diagnosed by rule 4, rule 5, rule 9, rule 12, rule 14, and rule 15 (or rule 17), respectively. (2) Fault isolation algorithm for mode 2

H. Wang et al. / Energy and Buildings 43 (2011) 1774–1783

Fig. 3. Fault isolation algorithm for operating mode 1.

Fig. 4. Fault isolation algorithm for operating mode 2.

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a

H. Wang et al. / Energy and Buildings 43 (2011) 1774–1783

a

90

120

80 100

70

Control signal of damper opening (%) Measured air flow rate Minimum air flow rate setpoint

50

Flow (L/s)

Flow (L/s)

60

Maximum air flow rate setpoint

40

Air flow rate setpoint

80 Control signal of damper opening (%) Measured air flow rate

60

30

Minimum air flow rate setpoint Required air flow rate

20 40 10 0 8:00:00

10:00:00

12:00:00

14:00:00

16:00:00

18:00:00

20 8:00:00

Time (h)

b

10:00:00

12:00:00

26

b 25

14:00:00

16:00:00

18:00:00

Time (h) 25

Measured zone temperature

24

Temperature (°C)

Temperature (°C)

24 Zone temperature setpoint

23

22

Zone measured temperature

23

Zone temperature setpoint

22

21 21 8:00:00

10:00:00

12:00:00

14:00:00

16:00:00

18:00:00

Time (h) Fig. 5. Too low maximum air flow rate setpoint.

20 8:00:00

10:00:00

12:00:00

14:00:00

16:00:00

18:00:00

Time (h) Fig. 6. Too high minimum air flow rate setpoint.

The fault isolation algorithm for mode 2 is shown in Fig. 4. The fault isolation algorithm first compares the measured zone air flow rate with its setpoint, when the measured zone air flow rate can approach its setpoint well, the air flow rate setpoint would be compared with the minimum air flow rate setpoint and the maximum air flow rate setpoint. If the air flow rate setpoint is higher than the minimum air flow rate setpoint and lower than the maximum air flow rate setpoint, VAV controller hard failure can be diagnosed by rule 8. When the air flow rate setpoint is at the minimum, the possible causes of the fault, including too high minimum air flow rate setpoint, too high zone temperature setpoint, and too small cooling load can be diagnosed by rule 7, rule 19, and rule 6, respectively. If the measured zone air flow rate could not approach its setpoint, the possible causes of this fault, including air flow rate sensor reading error, air flow rate sensor reading deviation to minimum, damper failure, too low supply air temperature of AHU, and VAV controller hard failure can be diagnosed by rule 9, rule 10, rule 11, rule 13, and rule 16 (or rule 18), respectively.

4. Validation and discussions The FDD strategy was tested and validated on real VAV airconditioning systems in the office building described in Section 2.1. The air-conditioning systems operate from 8:00 to 18:00 every work day. The operation data of all AHU systems and VAV terminals was collected and stored in a SQL server at 5 min intervals. A computer program based on the FDD strategy was developed and

connected to the SQL server. The computer program can access current operation data from the SQL server and in real time examine the operation states (normal or faulty) of all pressure-independent VAV terminals in the large-scale building. A lot of faults in the pressure-independent VAV terminals were detected and diagnosed by the computer program successfully. A few classical faults are presented below to illustrate the performance of the FDD strategy.

4.1. Example 1: too low maximum air flow rate setpoint Too low maximum air flow rate setpoint was detected in VAV terminal 6 at the 8th floor on July 26, 2010. Fig. 5(a) and (b) shows measured zone air flow rate, maximum air flow rate setpoint, minimum air flow rate setpoint, air flow rate setpoint, control signal of damper opening, measured zone temperature and zone temperature setpoint from VAV terminal 6 at the 8th floor. The measured zone temperature is higher than its setpoint. The measured zone air flow rate and its setpoint are both at the maximum. However, the maximum air flow rate setpoint is much lower than the manufacturer’s scheduled maximum air flow rate of the VAV terminal. The maximum air flow rate setpoint is lower than the appropriate air flow rate relative to the cooling load. The cause of this fault is too low maximum air flow rate setpoint. This fault will lead to higher zone temperature and unbearable indoor environmental conditions when the cooling load is bigger.

H. Wang et al. / Energy and Buildings 43 (2011) 1774–1783

a

a

110

90

70

Measured air flow rate

Flow (L/s)

Flow (L/s)

Control signal of damper opening (%)

Minimum air flow rate setpoint Maximum air flow rate setpoint

50

90

Control signal of damper opening (%) Measured air flow rate

70

1781

Air flow rate setpoint

50

Minimum air flow rate setpoint Air flow rate setpoint

30

30

10

10

-10 8:00:00

10:00:00

12:00:00

14:00:00

16:00:00

-10 8:00:00

18:00:00

10:00:00

12:00:00

b

14:00:00

16:00:00

18:00:00

16:00:00

18:00:00

Time (h)

Time (h)

b

27

26

26 25

24

Temperature (°C)

Temperature (°C)

25

Measured zone temperature Zone temperature setpoint

23

22

24

Measured zone temperature Zone temperature setpoint

23

22 21

20 8:00:00

10:00:00

12:00:00

14:00:00

16:00:00

18:00:00

Time (h) Fig. 7. Flow sensor reading deviation to minimum.

4.2. Example 2: too high minimum air flow rate setpoint Too high minimum air flow rate setpoint was diagnosed in VAV terminal 35 at the 24th floor on July 27, 2010. Fig. 6(a) and (b) shows measured zone air flow rate, maximum air flow rate setpoint, minimum air flow rate setpoint, air flow rate setpoint, control signal of damper opening, measured zone temperature and zone temperature setpoint from VAV terminal 35 at the 24th floor. The measured zone temperature is lower than its setpoint. The minimum air flow rate setpoint is 90 l/s. The measured zone air flow rate and its setpoint are both at the minimum. The minimum air flow rate setpoint is greater than the appropriate air flow rate relative to the cooling load. The cause of the fault is too high minimum air flow rate setpoint. The fault will lead to lower zone temperature and cooling energy waste when the cooling load is smaller. 4.3. Example 3: flow sensor reading deviation to minimum Flow sensor reading deviation to minimum was detected in VAV terminal 2 at the 27th floor on July 26, 2010. Fig. 7(a) and (b) shows measured zone air flow rate, maximum air flow rate setpoint, minimum air flow rate setpoint, air flow rate setpoint, control signal of damper opening, measured zone temperature and zone temperature setpoint from VAV terminal 2 at the 27th floor. The air flow rate setpoint is at the minimum. The control signal of damper opening is always 100%. However, the measured zone air flow rate is always 0 l/s. The measured zone temperature is much lower than its set-

21 8:00:00

10:00:00

12:00:00

14:00:00

Time (h) Fig. 8. Damper failure.

point. On-site verification showed that the fault was due to flow sensor reading deviation to minimum. 4.4. Example 4: damper failure Damper failure was detected in VAV terminal 32 at the 5th floor on July 26, 2010. Fig. 8(a) and (b) shows measured zone air flow rate, minimum air flow rate setpoint, air flow rate setpoint, control signal of damper opening, measured zone temperature and zone temperature setpoint from VAV terminal 32 at the 5th floor. The minimum air flow rate setpoint is 20 l/s, and the air flow rate setpoint is at the minimum. However, the measured zone air flow rate is much greater than the air flow rate setpoint. As a result, the measured zone temperature is lower than its setpoint. On-site verification showed that the fault was due to damper failure. The stuck damper allowed uncontrolled air flow rate into the zone, which caused the zone temperature to fall well below the setpoint. 4.5. Example 5: too low zone temperature setpoint Too low zone temperature setpoint was diagnosed in VAV terminal 19 at the 29th floor on July 26, 2010. Fig. 9(a) and (b) shows the measured zone air flow rate, maximum air flow rate setpoint, minimum air flow rate setpoint, air flow rate setpoint, control signal of damper opening, measured zone temperature and zone temperature setpoint from VAV terminal 19 at the 29th floor. The zone temperature setpoint is 0 ◦ C. The measured zone air flow rate and

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a

H. Wang et al. / Energy and Buildings 43 (2011) 1774–1783

a

130

130 Control signal of damper opening (%)

110

110

Measured air flow rate

90

Control signal of damper opening (%)

90

70

Minimum air flow rate setpoint

50

Air flow rate setpoint

Flow (L/s)

Measured air flow rate

Flow (L/s)

Minimum air flow rate setpoint Maximum air flow rate setpoint

Maximum air flow rate setpoint

70

Air flow rate setpoint

50

30 30

10 8:00:00

10

10:00:00

12:00:00

14:00:00

16:00:00

-10 8:00:00

18:00:00

10:00:00

Time (h)

14:00:00

16:00:00

18:00:00

16:00:00

18:00:00

Time (h)

b

25

b

12:00:00

25

24 20

Temperature (°C)

Temperature (°C)

23 15

Measured zone temperature Zone temperature setpoint

10

5

10:00:00

12:00:00

14:00:00

16:00:00

18:00:00

18 8:00:00

10:00:00

12:00:00

14:00:00

Time (h) Fig. 11. VAV controller hard failure.

Fig. 9. Too low zone temperature setpoint.

its setpoint are both at the maximum. When too low zone temperature setpoint was detected and diagnosed successfully at 14:45 pm, the zone temperature setpoint was reset to 24 ◦ C immediately. Both the measured zone air flow rate and its setpoint dropped little by little after the fault was eliminated. And then the zone temperature rose gradually.

130

110

90 Control signal of damper opening (%)

Flow (L/s)

21

19

Time (h)

70 Measured air flow rate

4.6. Example 6: air flow rate sensor reading error Air flow rate sensor reading error was detected in VAV terminal 30 at the 7th floor on July 26, 2010. Fig. 10 shows measured zone air flow rate, maximum air flow rate setpoint, minimum air flow rate setpoint, air flow rate setpoint and control signal of damper opening from VAV terminal 30 at the 7th floor. The air flow rate setpoint is always at the minimum. The control signal of damper opening is around 0. When the air-conditioning system operates, the measured zone air flow rate is greater than its setpoint. When the air-conditioning system does not operate, the measured zone air flow rate is much greater than 0. On-site verification showed that the fault was due to air flow rate sensor reading error. When the fault has been existed, the VAV terminal would provide inappropriate air flow rate into the zone to meet the cooling load. As a result, the zone temperature could not approach its setpoint well.

Minimum air flow rate setpoint

50

4.7. Example 7: VAV controller hard failure

Maximum air flow rate setpoint

30

Air flow rate setpoint

10

-10 00:00:00

Zone temperature setpoint

20

0

-5 8:00:00

Measured zone temperature

22

04:00:00

08:00:00

12:00:00

16:00:00

Time (h) Fig. 10. Air flow rate sensor reading error.

20:00:00

24:00:00

VAV controller hard failure was diagnosed in VAV terminal 24 at the 31st floor on July 26, 2010. Fig. 11(a) and (b) shows measured zone air flow rate, maximum air flow rate setpoint, minimum air flow rate setpoint, air flow rate setpoint, control signal of damper opening, measured zone temperature and zone temperature setpoint from VAV terminal 24 at the 31st floor. The air flow rate setpoint is at the maximum, but the control signal of damper opening is smaller. The measured zone air flow rate is

H. Wang et al. / Energy and Buildings 43 (2011) 1774–1783

lower than its setpoint. As a result, the measured zone temperature is much higher than its setpoint. On-site verification showed that the fault was due to VAV controller hard failure. When VAV controller hard failure has been existed, the VAV controller would provide improper control signal, which will prevent the subsystem from providing appropriate air flow rate into the zone. 5. Conclusions In this study, a robust fault detection and diagnosis strategy and its online program are developed for pressure-independent VAV terminals. The program is employed to online detect and diagnose the faults of 1186 VAV terminals in a real office buildings. The FDD strategy was tested and validated using in real time data from real VAV air-conditioning systems. In situ tests, the results of fault detection and diagnosis matched the on-site verification results well. The results of tests show that the residual-based CUSUM control chart is a robust tool for fault detection of VAV terminals. Also, the rule-based fault classifier developed is an efficient tool to find fault sources. The fault detection and diagnosis strategy can be conveniently implemented on real buildings as it relies only upon the rated parameters of terminals as well as sensor and control signals that are commonly available in EMCS. The strategy can provide a robust and effective tool for detecting and diagnosing faults in pressure-independent VAV terminals. Acknowledgments The research work presented in this paper is financially supported by Building Intelligent Control Research Fund (JRP0901) granted by Swire Properties Management Ltd. The authors would like to appreciate Honeywell International Inc. (Hong Kong) for the technical help to this research. References [1] P. Haves, Overview of diagnostic methods, in: Proceedings of Diagnostics for Office Buildings: From Research to Practice, 1999, San Francisco, CA. [2] J. Schein, S.T. Bushby, N.S. Castro, J.M. House, A rule-based fault detection method for air handling units, Energy and Buildings 38 (2006) 1485–1492. [3] J.Y. Qin, S.W. Wang, A fault detection and diagnosis strategy of VAV airconditioning systems for improved energy and control performances, Energy and Buildings 37 (2005) 1035–1048.

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