An expert rule-based fault diagnosis strategy for variable refrigerant flow air conditioning systems

Applied Thermal Engineering 149 (2019) 1223–1235

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Applied Thermal Engineering journal homepage: www.elsevier.com/locate/apthermeng

Research Paper

An expert rule-based fault diagnosis strategy for variable refrigerant flow air conditioning systems

T

Yabin Guoa, Jiangyu Wanga, Huanxin Chena, , Guannan Lib, Ronggeng Huanga, Yue Yuanc, Tanveer Ahmada, Shaobo Suna ⁎

a

Department of Refrigeration & Cryogenics, Huazhong University of Science and Technology, Wuhan, China School of Urban Construction, Wuhan University of Science and Technology, Wuhan, China c China-EU Institute for Clean and Renewable Energy, Huazhong University of Science and Technology, Wuhan, China b

HIGHLIGHTS

fault diagnosis strategy is proposed for the VRF system. • A22rule-based rules are developed through expert knowledge and characteristics of the VRF system. • Theexpert diagnosis strategy is evaluated with data of nine faults types under cooling mode. • Ninefault • faults are well diagnosed by the rule-based fault diagnosis strategy. ARTICLE INFO

ABSTRACT

Keywords: Fault diagnosis Variable refrigerant flow Expert rules Air conditioning Energy saving

This paper proposed a novel fault diagnosis strategy of the variable refrigerant flow (VRF) system based on expert rules for the first time. The VRF fault diagnosis rules (VFDR) are obtained through the expert knowledge and characteristics of the VRF system. The VFDR includes a total of 22 expert rules, 10 rules for the outdoor unit and other 12 rules for the indoor unit. The proposed VFDR can help maintenance personnel to identify and eliminate VRF faults in time. In addition to obtaining the coefficients of the sensor regression model, the VFDR does not require a training process and is computationally simple, making it easily embedded in the building’s automatic control system to achieve online fault diagnosis. The proposed fault diagnosis strategy is validated with nine faults of the VRF system under cooling mode. These faults contain temperature sensor faults, the system fault and indoor unit faults. The diagnosis correct rate (DCR) is used to evaluate the performance of the VFDR. Through expert rules fault diagnosis strategy, the DCRs of all faults exceed 70% and the overall DCR of all faults is 85.13%. The results show that faults of VRF system are well diagnosed by fault diagnosis strategy based on expert rules.

1. Introduction Building energy consumption accounts for a large part of terminal energy consumption. Building energy consumption occupies 41% of terminal energy consumption in the United States [1], and in Europe this proportion is about 40% [2]. While in China, building energy consumption also accounts for about 20% of terminal energy consumption [3]. Heating, ventilation and air conditioning (HVAC) system is the main energy consumption equipment in buildings, which is estimated to nearly 40% of total energy consumption in the commercial building [4]. Faults of the HVAC systems will result in a large amount of energy waste, decrease the service life of equipment and deteriorate

⁎

indoor thermal comfort. Faults of the HVAC system will result in 25–50% of the energy wasted through the investigation of UK building consumption. But if faults are diagnosed and maintained in advance of irreversible device damages, this energy waste can be reduced to less than 15% [5]. Therefore, the research on the fault diagnosis of HVAC systems is of great significance for energy-saving operation of the system, increasing the service life of the equipment and ensuring indoor thermal comfort. There have been many studies on fault diagnosis for HVAC systems. (1) Chiller: Wang et al. [6] proposed a fault diagnosis strategy using the Bayesian network for the chiller system. Yan et al. [7] proposed a costsensitive and sequential feature selection strategy through a back-

Corresponding author. E-mail address: [email protected] (H. Chen).

https://doi.org/10.1016/j.applthermaleng.2018.12.132 Received 16 April 2018; Received in revised form 13 November 2018; Accepted 24 December 2018 Available online 27 December 2018 1359-4311/ © 2018 Elsevier Ltd. All rights reserved.

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Nomenclature

EXVheating heating electronic expansion valve opening EXVsubc electronic expansion valve opening corresponding to the subcooler fcom compressor operational frequency, Hz fcom, T compressor operational target frequency, Hz fod, fan outdoor fan operational frequency, Hz IDU indoor unit Iod, fan outdoor fan operational electric current, A Pcond condensing saturation pressure, MPa Pevap evaporating saturation pressure, MPa T temperature, °C

df in id L on od off sh subc sc spr out V

Subscript

Greeks

com dis

compressor discharge

µ

tracing sequential forward method. This strategy was used to select the most important features for real-work application. A novel adaptive Gaussian mixture model strategy is established for FDD in a watercooled multi-chiller plant system [8]. The Extended Kalman Filter model and the recursive one-class support vector machine (SVM) are combined to establish the online fault detection strategy for the chiller. The training process of the proposed strategy does not require any faulty data [9]. A chiller FDD method based on Bayesian network combined with the distance rejection and the multi-source information is proposed [10]. (2) VAV: For the VAV boxes fault detection and isolation, a novel linear causal model of multiple actuators and multiple sensor faults is designed and implemented. The proposed strategy shows superior fault detection and isolation performance compared to the statistical model [11]. Wang and Chen [12] proposed a robust FDD strategy for the VAV system through the residual-based exponentially

deforest inlet pipe indoor liquid refrigerant the ON mode of the IDU outdoor the OFF mode of the IDU shell subcooler subcooling degree vapor-liquid separator outlet pipe vapor refrigerant

the threshold of the rule measured value

weighted moving average control chart and the rule-based methods. Xiao et al. [13] established an FDD model using the diagnostic Bayesian network for the VAV system and the proposed strategy can work well with uncertain and incomplete information. (3) AHU: Zhao et al. [14,15] proposed a diagnostic Bayesian networks-based method for the most of common faults (dampers, fans, filters, coils and sensors) in AHUs and results show that the proposed strategy effectively diagnosed AHU faults. A multiple-model fault diagnosis approach is developed for the AHU system using the Bayesian network and constructed based on knowledge regarding component interdependencies and conservation laws [16]. The unsupervised SVM detection and Gaussian process models are used to detect the faults of the AHU system [17]. Turner et al. [18] developed a data-driven fault detection method based on the recursive least-squares model approach for AHU systems. And the fault detection method only requires indoor and outdoor temperatures. Dey

Fig. 1. Schematic of the test VRF system under cooling mode.

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discovered in time. Therefore, it is possible to establish an easy-to-use expert knowledge library for operation and maintenance personnel to use. This will make it possible to determine whether the current operating state of the HVAC system is normal without comprehensive expert knowledge. House et al. [26] first proposed an expert rule set for the AHU system in 2001. The proposed expert rule set contains 28 rules under different operating conditions. A few years later, Schein et al. [27] extended this work by evaluating the FDD performance of expert rule set using commonly found mechanical and control faulty data under a variety of weather conditions, system types and usage patterns. Then, Wang et al. [28] proposed an FDD strategy combining model-based and rule-based FDD methods for the AHU system. Xiao et al. [29] proposed an enhanced PCA method with expert-based multivariate decoupling for sensor FDD in the VAV system. A fault diagnosis strategy based on a simple regression model and an expert rule set is developed for chillers [30]. However, there is no comprehensive and quantitative fault diagnosis strategy based on expert knowledge for the VRF system. Therefore, this study proposes VRF faults diagnosis rules (VFDR) based on quantitative expert knowledge. The VFDR consist of a total of 22 expert rules. 10 rules are developed for outdoor unit (ODU) and 12 rules are for indoor unit (IDU). The proposed fault diagnosis strategy is validated with nine faults of the VRF system under cooling mode. The faults contain temperature sensor faults, the system fault and indoor unit faults. The diagnosis correct rate is used as an evaluation index to quantify the performance of the VFDR fault diagnosis strategy. The proposed VFDR strategy has good reliability for the VRF system type in this paper, but it should be noted that the VFDR strategy cannot be applied to all types of VRF system at present.

Fig. 2. The scheme of VRF system faults classification.

and Dong [19] developed a new way to detect and diagnose faults in the AHU system through combining Bayesian Belief Network and performance assessment rules. (4) Other: The PCA models are established to detect the sensor faults and the clustering method is used to classify and recognize the various operation conditions adaptively [20]. Janecke et al. [21] proposed the static and dynamic FDD metrics for both sub-critical and transcritical refrigeration cycles. Kim and Braun [22] presented three virtual refrigerant mass flow sensors for FDD of the water-to-water heat pump, the residential air split system and commercial packaged systems. But research on the VRF system is not comprehensive and intensive. Due to its flexible installation, space saving, advanced control and energy saving, the VRF system has been widely used in small and medium-sized buildings. Therefore, more and more studies are beginning to focus on the VRF system. Shin et al. [23] developed two fault detection techniques for FDD of the VRF system. Two techniques are applied to detect heat exchanger fouling and valve sticking based on a state observer and temperature variance. The optimized back propagation neural network method is used to establish the fault diagnosis model for the VRF system. The feature variable set is optimized based on the data mining method [24]. Guo et al. [25] proposed an enhanced sensor FDD method based on the Satizky-Golay method and the principal component analysis (PCA) method for the VRF system. Through the above review of various types of HVAC system fault diagnosis studies, there are few researches on fault diagnosis using expert rules strategy. Experts in the field of the HVAC system can determine whether the system suffers from fault based on their own expert knowledge and the system operational parameters and analyze what kind of faults have occurred. However, most HVAC system maintenance personnel do not have comprehensive expert knowledge. Some faults may not be

2. VRF system and faults description 2.1. VRF system description The fault diagnosis strategy proposed in this paper is applied to VRF systems. Fig. 1 illustrates the schematic of the test VRF system under cooling mode. The VRF system consists of two parts which are ODU and IDUs respectively. The ODU contains the compressor, outdoor side heat exchanger, vapor–liquid separator and other major components. The IDU mainly includes the indoor side fan coil, electronic expansion valve and other components. The outdoor environment temperature sensor, the compressor discharge temperature sensor and the vapor–liquid separator inlet temperature sensor are installed on the corresponding positions of the ODU to collect the status parameters and working condition information during the operation of the VRF system. The refrigerant of the VRF system is R410A and its standard refrigerant charge is 9.9 kg. The rated capacities of five IDUs are 2.8 kW, 3.6 kW,

Table 1 Different faults of the VRF system. No.

Fault type

Fault description

1 2 3 4

Tcom, dis sensor detached Tcom, dis sensor bias Tspr , in sensor detached Tspr , in sensor bias

Temperature sensor dropped from the refrigerant pipe There is an indefinite deviation between the real value and the measured value of temperature sensor Temperature sensor dropped from the refrigerant pipe

9

Indoor unit fouling

5 6 7 8

Tod sensor bias Refrigerant undercharge EXV stuck closed fault EXV stuck at intermediate position fault

There is an indefinite deviation between the real value and the measured value of temperature sensor

There is an indefinite deviation between the real value and the measured value of temperature sensor Refrigerant charge level is 63.64% EXV sticking and fixed at the closed position. The EXV stuck closed fault is introduced to IDU 2 EXV is fixed at 50% opening degree relative to the maximum opening. The EXV stuck at intermediate position fault is introduced to IDU 3 The IDU heat exchanger air-side fouling fault is introduced to IDU 4 and 5 simultaneously

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Fig. 3. Diagram of pressure-enthalpy under normal and refrigerant undercharge conditions.

5.0 kW, 7.1 kW and 11.2 kW respectively. Experiments were carried out in a standard psychrometer testing laboratory. The laboratory contains two rooms that can control temperature and humidity, respectively, to simulate indoor room and outdoor environment conditions. In this paper, the working conditions of the fault experiments are: outdoor side dry bulb and wet bulb temperatures are 35 °C and 24 °C; indoor room dry bulb and wet bulb temperatures are 26 °C and 19 °C. All the variables are collected by the sensors from the original equipment manufacturer. The data collected by these sensors is transferred to the VRF system control board, then the data can be transferred to the computer in real time and saved. The sample interval in this study is 3 s. The detailed introduction of fault experiments will be introduced in Section 2.2.

position. For the EXV stuck at intermediate position fault, the EXV opening of IDU is fixed at 50% opening degree relative to the maximum opening. Therefore, the EXV is no longer automatically adjusted to the opening degree with the changing load. The IDU fouling fault is simulated by blocking the air inlet area of the IDU to reduce the air flow rate. The data of fault experiments were collected and used to validate the expert rule-based fault diagnosis strategy proposed in this paper. 3. Robust fault diagnosis strategy for VRF systems This section first introduces three sensor models established based on multiple linear regression (MLR) method. Then the rules related to the sensor faults, the system fault and the IDU faults are introduced respectively. Finally, two validation indexes are introduced.

2.2. VRF system faults Expert rules for fault diagnosis are focused on temperature sensor faults, the system fault and indoor unit faults under cooling mode. These faults can cause system control failures, reduced indoor comfort, wasted energy and long-term operation can also damage the VRF system hardware. Therefore, it is necessary to detect and diagnose the fault in time. Fig. 2 presents the scheme of VRF system faults classification. Sensor faults consist of two type of faults which include bias and detached. The Tcom, dis , Tspr , in and Tod are selected to introduce these faults. The system fault is the refrigerant undercharge fault. As for IDU faults, there are electronic expansion valve (EXV) faults and indoor unit fouling. Moreover, EXV faults include stuck closed and intermediate position faults. Table 1 details nine faults and their descriptions of VRF system under cooling mode. For the sensor detached fault, it is simulated by artificially taking off the sensor. And the sensor is manually placed in another heat source for simulating the bias fault. The measured temperature deviates from the actual temperature value. The VRF system refrigerant is artificially charged to 63% of the standard refrigerant amount to achieve the refrigerant undercharge fault. For the EXV stuck closed fault, the EXV opening of the IDU is fixed at the closed

3.1. Prediction models of different sensors In order to determine the criterion of sensor measurement, three temperature sensor models of Tcom, dis , TSPR, in and Tod are established through MLR method. MLR is a regression analysis method describing the linear relationship between a dependent variable and multiple explanatory variables. The general form of MLR is shown in Eq. (1):

Y=

0

+

1 X1

+

2 X2

+

3 X3

+

+ j Xj +

+ k Xk + µ

(1)

where k is the number of explanatory variables. j (j = 1, 2, , k ) is the regression coefficient, and µ is the random error except for the effect of the independent variables on the Y. The matrix expression of n stochastic equations represented by Eq. (1) is shown in Eq. (2).

Y=X +µ

(2)

The input variables and prediction output variables of the model are shown in Eqs. (3)–(5), respectively. Each model contains 17 input variables.

1226

Refrigerant undercharge

EXV stuck closed

EXV stuck at intermediate position

Indoor unit fouling

7

8

9

Tspr , in sensor bias

4

6

Tspr , in sensor detached

3

Tod sensor bias

Tcom, dis sensor bias

2

5

1

Tcom, dis sensor detached

1

1227

22

20 21

19

18

17

10 11 12 13 14 15 16

9

8

7

6

5

4

3

2

Rule NO.

Fault type

Faults No.

Table 2 Rules of VFDR strategy under cooling mode.

Tcom, dis | >

>

>

p, evap

d, off

&|Tspr , in

&|Tod

& |Tspr ,in

& |Tspr ,in

d, off

p

p

TIDU ,in, on > TIDU ,in, on <

TIDU , out, on TIDU , out, on

TIDU , out, fault

TIDU , in,on | <

|TIDU , out, off i, l

i, l

TIDU ,out ,normal < 0

i, f

i, o, l

TIDU ,in, on | <

|TIDU , in,off

s

s

Tod | > d, off

Tspr , in | >

Tod | >

Tod|

& System shutdown

& System shutdown

& System shutdown

d, off

s

s

d, off

Tod | >

Tod|

Tcom,dis | >

& |Tcom, dis

& |Tcom, dis

&|Tspr , in

p

p

Tsc sc ALLTIDU , out, on TIDU ,in, on > o, i, r ALLTid TIDU , out, on < id, o,r Tid TIDU , in,on < i, s Tid TIDU , out, on < i, s TIDU , in, on TIDU , in, on, other > i, io, s TIDU , in, off , other TIDU , in, off > i, io,l TIDU , out, off , other TIDU ,out ,off > i, io, l

Pevap <

p

Tod | >

>

>

>

d, off

Tspr , in | >

Tout , pr Tod

|Tcom, dis

Tod

|Tcom, dis

Tspr , in Tspr , in, pr Tspr , in

Tspr , in Tspr , in, pr Tspr , in

|Tod

Tcom, dis Tcom, dis, pr Tcom, dis

Tcom, dis Tcom, dis, pr Tcom, dis

Rule expression

o, i, r .

and the outlet refrigerant temperature will approximately equal to the indoor temperature

be lower than that of normal IDU

For this fault, it is expected that the temperature difference between the outlet and inlet refrigerant temperatures of IDUs will exceed a threshold i, f . Besides, the outlet refrigerant temperature of faulty IDU will

i, o, l

For this fault, it is expected that the inlet and outlet refrigerant temperatures of faulty OFF IDU are significantly lower than the temperature of other OFF IDUs. The inlet and outlet refrigerant temperatures of faulty OFF IDU will be similar to the inlet refrigerant temperature of the running IDU. Besides, the temperature difference between the outlet and inlet refrigerant temperatures of the running IDU will exceed a threshold

For this fault, it is expected that the inlet and outlet refrigerant temperatures of the IDU will be close to the indoor temperature. Besides, the inlet refrigerant temperature of faulty IDU will be significantly higher than that of other normal IDU

threshold

Besides, when the system is shutdown, the outdoor temperature will be significantly different from the other two sensors For this fault, it is expected that evaporating saturation pressure and subcooling should be less than or equal to their respective thresholds p,evap and sc , respectively. The temperature differences of the IDUs will exceed a

outdoor temperature Besides, when the system is shutdown, the vapor-liquid separator inlet temperature will be significantly different from the other two sensors For this fault, it is expected that the relative error between the measured and predicted sensor value will exceed a threshold p

outdoor temperature For this fault, it is expected that the relative error between the measured and predicted sensor value will exceed a threshold p and the vapor-liquid separator inlet temperature will not be approximately equal to the

outdoor temperature Besides, when the system is shutdown, the compressor discharge temperature will be significantly different from the other two sensors For this fault, it is expected that the relative error between the measured and predicted sensor value will exceed a threshold p and the vapor-liquid separator inlet temperature will be approximately equal to the

temperature For this fault, it is expected that the relative error between the measured and predicted sensor value will exceed a threshold p and the compressor discharge temperature will not be approximately equal to the

For this fault, it is expected that the relative error between the measured and predicted sensor value will exceed a threshold p and the compressor discharge temperature will be approximately equal to the outdoor

Rule description

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3.2. VRF faults diagnosis rules (VFDR) 3.2.1. Rules of sensor faults The control and protection mechanisms of the VRF system highly rely on a variety of sensors to collect real-time operating parameters. Thus, the accurateness and reliability of sensors should be guaranteed for the purpose of the stable operation of VRF system. In this paper, an expert rule set for the sensor fault diagnosis is developed by establishing temperature sensor prediction models and relying on the redundant information of sensors. The sensor faults studied in this paper mainly include two categories, detached and bias. The studied temperature sensors are Tcom, dis , TSPR, in and Tod . During long-term operation of the VRF system, sensor detached faults may occur due to machine vibration, etc. In this case, the sensor itself does not malfunction, but the measured value is not the desired temperature measurement. According to the special case of the temperature sensor detached fault, it can be found that when the sensor falls off, its temperature measurement value should be close to the outdoor environment temperature. Thus rules like |Tcom, dis Tod| s can be obtained. Besides, the measured value changes will lead to changes in the coupling between the various sensor measurements. That is, each temperature sensor’s prediction model is established through the data of the VRF system during normal operation. When all sensors are in a normal condition, the deviation between the model predictions and the sensor measurements is supposed to be within a certain range. When a sensor detached fault occurs, its model predictive value will deviate from the sensor measurements and a rule of the form

Fig. 4. The relative error of three prediction models for temperature sensors.

Model 1

Tcom, dis

Tcom, dis, pr

Tcom, dis

, fcom , fod, fan , Pcond, Pevap, EXVheating , EXVsubc , Icom, Iod, fan ) + (3) Model 2 Tcom, dis=f (Tod, TSPR,in, Tcom, sh, Tdf , Tsubc, out, L, Tsubc, out, V , TSPR,out , Tcom, Tod, fan , fcom , fod, fan , Pcond, Pevap, EXVheating , EXVsubc , Icom, Iod,fan ) +

(4)

Model 3 TSPR,in=f (Tod, Tcom,dis, Tcom, sh, Tdf , Tsubc, out, L, Tsubc, out, V , TSPR,out , Tcom, Tod, fan , fcom , fod, fan , Pcond, Pevap, EXVheating , EXVsubc , Icom, Iod,fan ) +

>

p

can be obtained. In general, these two rules will

appear at the same time when the sensor detached, so rule1 and rule4 are proposed for Tcom, dis and Tspr , in sensors respectively in this paper. Because the measured value deviates from the actual value when the temperature sensor bias fault occurs, the predicted value of the model will also deviate from the sensor’s measured value similar to the temperature sensor detached fault. In additional, the sensor measurements don’t approximate to the outdoor environment temperature. This feature can be used to distinguish it from sensor detached fault. Combining these two points, rule 2 and rule 5 are obtained for Tcom, dis and TSPR, in sensors. Due to the particularity of the outdoor temperature sensor, the fault diagnosis rule of the bias fault is rule 7. In order to further confirm the sensor’s bias fault, it is possible to utilize the own sensor redundancy of the VRF system, that is, when the system is shutdown, the measured values of the ODU temperature sensors should

Tod=f (TSPR, in, Tcom, dis, Tcom, sh, Tdf , Tsubc, out , L, Tsubc, out , V , TSPR, out , Tcom, Tod, fan

(5)

Table 3 The threshold settings of expert rules. Threshold parameters The relative error threshold of sensor prediction models

p

The minimum temperature difference between the outdoor and other temperature sensor s The maximum sum of the temperature difference between different temperature sensors d,off The minimum value of evaporating pressure The The The The The The The The The

1228

Rules No.

0.15

1, 2, 4, 5, 7

0.8 MPa

9

2 °C 2 °C

p, evap

minimum value of subcooling sc maximum temperature difference of IDU inlet and outlet o, i, r minimum temperature difference between indoor and IDU outlet temperatures id, o, r minimum temperature difference between indoor and IDU coil temperatures i, s maximum temperature difference of different IDU inlet temperatures i, io, s maximum temperature difference of different IDU inlet and outlet refrigerant temperatures under OFF mode minimum temperature difference of IDU coil (OFF mode) and inlet (ON mode) temperatures i, l maximum temperature difference of IDU outlet and inlet refrigerant temperatures under ON mode i, o,l minimum temperature difference of IDU outlet and inlet refrigerant temperatures under ON mode i, f

Value

i, io, l

1 °C 15 °C 5 °C 5 °C 10 °C 10 °C 3 °C 9 °C 1 °C

1, 2, 4, 5 3, 6, 8 10 11 12 13, 14 15 16, 17 18, 19 20 21

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Fig. 5. Outdoor temperature sensor bias fault under cooling mode.

Fig. 6. Compressor discharge temperature sensor bias fault under shutdown mode.

be similar and approach the outdoor temperature. The bias fault sensor can be identified by the three temperature sensors. Therefore, the rule3, rule 6 and rule 8 are obtained under the shutdown state of the VRF system. The sensor detached fault diagnosis is only applicable to the ODU sensors. The sensors of the IDU detached fault can’t use the rules proposed in this study to diagnose. But these sensors can attempt to perform diagnostic studies in comparison with the indoor temperature sensor.

diagram, it can be seen that when the condensing pressure is decreased, the saturation temperature corresponding to the condensing pressure is reduced, and thus the subcooling degree of the system is reduced or even zero. Based on the thermodynamic cycle of the system, the rule 9: Pevap < p, evap and rule 10: Tsc sc can be obtained. Just based on these two rules is not enough to be very sure that the system has the refrigerant undercharge fault. At this time, it can be considered from the IDU side of the VRF system. Due to lack of refrigerant, the flow of refrigerant into each IDU is reduced. Although the evaporator inlet temperature decreases, the smaller flow of refrigerant is not enough to carry the heat away from the room. The temperature difference of the IDU will increase from the analysis of the inlet and outlet refrigerant temperature of the IDU. Besides, the outlet refrigerant temperature will be close to the indoor temperature at the same time. The rule 11: TIDU , out , on TIDU , in, on > o, i, r and rule 12: Tid TIDU , out , on < id, o, r can be

3.2.2. Rules of the system fault Another fault that can easily occur in long-term operation of the VRF system is the refrigerant undercharge. As the amount of refrigerant in the system is insufficient, the condensing pressure and evaporating pressure are reduced as shown in Fig. 3. From the pressure-enthalpy

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IDUs, but the faulty IDU does not have the cooling capacity at this time. and rule 17: So rule 16: TIDU , in, off , other TIDU , in, off > i, io, l TIDU , out , off , other TIDU , out , off > i, io, l can be obtained. Besides, the low temperature refrigerant flows through the coil of OFF IDU and there is no flowing air for heat exchange, the inlet and outlet refrigerant temperatures of the faulty IDU will be similar to the inlet refrigerant temperature of the running IDU. That is, rule 18: |TIDU , in, off TIDU , in, on | < i, l and rule 19 |TIDU , out , off TIDU , in, on | < i, l two rules. Because refrigerant flow through the faulty IDU, thus the refrigerant flow of other running IDU will be reduced. This will lead to the temperature difference between running IDU inlet and outlet increase, that is, rule 20: TIDU , out , on TIDU , in, on > i, o, l . Through the above analysis, these five rules can be used to identify the IDU EXV stuck at intermediate position fault. (2) The IDU fouling fault The IDU fouling will result in the reduction of the air flow and increased heat transfer resistance, thus the heat exchange performance of IDU will be worse. Judging from the operating parameters, the most direct symptom is that the temperature difference between the inlet and outlet of the faulty IDU decreases, that is, rule21: TIDU , out , on TIDU , in, on < i, f . On the other hand, because the reduction of the air flow rate of the faulty IDU, the heat transfer of the IDU will be reduced. The outlet temperature of the faulty IDU is lower than that of normal IDU, that is rule 22: TIDU , out , fault TIDU , out , normal < 0 . These two rules can be used to identify the IDU fouling fault and can also identify which IDU fault. For the IDU fouling, all IDUs of the VRF system may have the fouling fault at the same time, then the application of rule 22 will have certain limitation. 3.2.4. VFDR rules set for fault isolation Sections 3.2.1–3.2.3 analyze each type of fault. The 22 rules are listed in Table 2 according to the fault category. This paper studies a total of nine kinds of faults under cooling mode. When a rule is satisfied, it indicates that the specific fault has occurred. Rules 1–8 are related to sensor faults, rules 9–12 are related to the system fault and rules 13–22 are related to IDU faults. Threshold selection results of each rule will be introduced in Section 4.1.

Fig. 7. Compressor discharge temperature sensor detached fault under cooling mode.

obtained from the IDU side. Therefore, according to the above four rules, the refrigerant undercharge fault of the VRF system can be identified. 3.2.3. Rules of indoor unit faults (1) EXV valve faults

3.3. Validation indexes

EXV valve faults of IDU mainly contains two cases. One is the EXV stuck closed fault, that is, the opening of EXV is fixed at the closed position when the IDU is running. The symptom caused by this fault is that there is no refrigerant flowing through the fan coil of the faulty IDU. But the fan of IDU is running. Therefore, the inlet and outlet refrigerant temperatures of the IDU will be close to the indoor temperature. Based on these characteristics, the rule 13: Tid TIDU , in, on < i, s and rule 14: Tid TIDU , out , on < i, s can be obtained. Because there is no refrigerant flow in the faulty IDU, it will cause the inlet refrigerant temperature of faulty IDU to be significantly higher than that of other normal IDU. The rule 15: TIDU , in, on TIDU , in, on, other > i, io, s is obtained. Another fault is the EXV stuck at intermediate position. After the fault occurs, although the IDU does not work, some of the low temperature refrigerant flows through the fan coil and the fan does not operate. As a result, the inlet and outlet refrigerant temperatures of faulty IDU are significantly lower than the temperature of other OFF

The relative error is used to evaluate the performance of the sensor prediction models and an important parameter to determine whether the sensor is faulty. The calculation formula is shown in Eq. (6). re

=

x

µ (6)

µ

where x is the predicted value and µ is the measured value. In order to quantitatively evaluate the fault diagnosis performance based on VFDR strategy, this paper developed the diagnosis correct rate (DCR) to evaluate the strategy. The DCR can be calculated as shown in Eq. (7)

DCR = DCR =

1230

Numbersofsamplesmeetingsinglerule Numberofactualfaultysamples

Numbersofsamplesmeetingmultiplerules Numberofactualfaultysamples

for single rule for multiple rules

(7)

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Fig. 8. Refrigerant undercharge fault under cooling mode.

4. Validation and discussions

mode, compressor discharge temperature sensor bias under system shutdown mode and detached under cooling mode faults. Besides, the DCR of each sensor fault is listed in Section 4.6.

4.1. Validation of regression models and the threshold settings The data from the normal VRF system is collected to train the sensor models. The normal operating condition is the same as that of fault experiments. The outdoor temperature fluctuates from 34 to 36 °C and all five IDUs work. A total of about 2000 normal samples are collected. Fig. 4 demonstrates the predictive performance of the three temperature sensor prediction models using training data. The predictive performance of models using validation data can be found in Section 4.2. The prediction relative error of Tod sensor model is small, while the prediction error of TSPR, in model is large. Overall, their relative error is basically within 0.15. The vapor-liquid separator inlet pipe temperature fluctuates greatly during operation, so the error of prediction result also fluctuates. By comparing the prediction errors of the three temperature sensor models, the threshold p of the rules established based on the prediction model performance is set to 0.15. Other thresholds are obtained through expert knowledge and operational parameters under normal operating conditions. The conservative values of the rule thresholds are selected to avoid false alarms. Table 3 lists threshold settings of 22 expert rules in this study.

4.2.1. Outdoor temperature sensor bias under cooling mode Fig. 5 exhibits the results of the Tod sensor bias fault under cooling mode. The sensor bias fault is introduced around sample point 510. The measured value of the outdoor environment temperature suddenly increases, while its predicted value shows a decrease first and then increases and remains stable. As the VRF system detects a sudden increase of the measured outdoor environment temperature, it causes a series of changes in the operating status of the system, resulting in a decrease in the predicted value. The predicted temperature remains stable after the system operation is stable. From the relative error of prediction, it can be concluded that the relative error of outdoor environment temperature becomes a large negative value after the bias fault, and most of them exceed the threshold p , which satisfies rule 7. The fault diagnosis strategy based on expert rules can identify outdoor environment temperature bias fault. 4.2.2. Compressor discharge temperature sensor bias fault under system shutdown mode The fault diagnosis may not work well under the condition of drastic change of working condition when the sensor bias fault is isolated by rule 7. Fig. 6 displays the results of compressor discharge temperature sensor bias fault under system shutdown mode. It can be seen from the figure that during normal operation, the temperature measurement of

4.2. Sensor faults diagnosis results Three typical temperature sensor faults are introduced in this section, which include outdoor temperature sensor bias under cooling

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after being stabilized. The two fault symptoms described above comply with rule 1, so compressor discharge temperature detached fault can be identified by proposed VFDR strategy. 4.3. Refrigerant undercharge fault under cooling mode Fig. 8 depicts the results of the refrigerant undercharge fault under cooling mode. It can be seen from the figure (a) that the evaporation pressure is lower than the threshold, which satisfies rule 9. As shown in figure (b), in the case of the insufficient refrigerant charge, the degree of subcooling decreases, which is lower than the threshold value. This parameter changes in line with rule 10. Figure (c) presents the trend of the temperature difference between the inlet and outlet pipes of each IDU. The temperature differences of IDUs under fault condition increase far beyond the normal temperature difference. Besides, there are two fluctuations in the temperature difference between the inlet and outlet of the IDU5, as shown in the red circled part of the figure. This situation emerges because EXV of IDU5 closed twice in the process, which caused fluctuations. Figure (d) demonstrates the temperature difference between the indoor temperature and the outlet pipe temperature of IDU. The temperature difference is lower than the threshold, indicating that the outlet temperature of IDU is quite similar to the indoor temperature. On the other hand, the IDU is almost lost its refrigeration ability, of which this phenomenon is in line with rule 12. The trend of the above four parameters respectively satisfies the expert rules 9–12 proposed in this paper, that is, the refrigerant undercharge can be identified through these rules of VFDR strategy. 4.4. EXV valve faults 4.4.1. EXV stuck closed fault under cooling mode Fig. 9 exhibits the results of EXV stuck closed fault under cooling mode. Figure (a) displays the trend of the inlet and outlet pipe temperature of two running IDUs. When there is no fault, the inlet and outlet pipe temperatures of two IDUs are not of much difference. But after the fault introduced around the sample point 1050, temperatures of inlet and outlet pipes of the faulty IDU are gradually increased because there is no flow of refrigerant. According to rule 13 and 14, the temperature differences between the indoor temperature and IDU coil (inlet and outlet) temperature are analyzed respectively. The result is shown in figure (b). When the fault occurs, the temperature differences are getting smaller and smaller, which are lower than the threshold after the stabilization. Figure (c) shows the trend of the temperature difference between the inlet pipe temperatures of faulty IDU and other normal IDU. When the EXV stuck closed fault occurs, the temperature difference increases rapidly. The inlet temperature of faulty IDU gradually increased and higher than that of other IDUs, which also indicates that there is no flow of refrigerant of faulty IDU. These fault symptoms satisfy rule 13–15. The EXV stuck closed fault can be diagnosed through VFDR strategy. Moreover, the VFDR can identify which IDU has failed.

Fig. 9. EXV stuck closed fault under cooling mode.

each sensor is equality or the phase deviation is within 2 °C. When a bias fault is introduced around sample point 310, the measured value of the compressor discharge temperature sensor suddenly increases, which is significantly different from the other sensor measurements. Fig. 6(b) illustrates the differences between the sensors. When the fault is introduced, their values significantly exceeds the threshold and satisfies the rule 3. Therefore, fault diagnosis of bias fault can be performed through multiple temperature sensors under the system shutdown mode. 4.2.3. Compressor discharge temperature sensor detached fault under cooling mode Fig. 7 shows the results of compressor discharge temperature detached fault under cooling mode. It can be seen from Fig. 7(a) that the deviation of predicted value and measured value of the compressor discharge temperature is small under normal condition. When the detached fault is introduced, the measured value rapidly decreases and gradually approaches the outdoor environment temperature. It can be seen from the Fig. 7(b) that the relative error of the predicted value exceeded the threshold when the fault of compressor discharge temperature detached is introduced. The deviation between the compressor discharge temperature and the outdoor temperature is getting smaller and smaller, and finally, it is basically equal to the outdoor temperature

4.4.2. EXV stuck at intermediate position fault under cooling mode Fig. 10 illustrates the results of the EXV stuck at intermediate position fault under cooling mode. The changing trends of inlet and outlet pipe refrigerant temperatures of two IDUs with OFF mode are shown in figure (a). The EXV stuck at intermediate position fault is introduced around the sample point 500. The inlet and outlet pipe refrigerant temperatures of faulty IDU rapidly decreases and then tend to be stable gradually. The coil temperature difference between the faulty IDU and normal IDU shown in figure (b). The inlet temperature difference and

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Fig. 10. EXV stuck at intermediate position fault under cooling mode.

outlet temperature difference of different IDUs are small before the fault is introduced. But after introducing the EXV stuck at intermediate position fault, they increase rapidly and exceed the threshold. This phenomenon is in line with rule 16 and 17. Figure (c) depicts the trends of the temperature differences between the coil temperature of the faulty IDU and the inlet temperature of the normal running IDU. The temperature differences are very large before the fault is introduced, and there is no refrigerant flowing through the IDU. But after the fault is introduced, the inlet and outlet refrigerant temperatures of the faulty IDU are closed to the inlet temperature of the running IDU. This satisfies rule 18 and 19. On the other hand, the faulty IDU will cause the operation change of the other IDUs. It can be seen from figure (d) that the refrigerant flowing through the running IDU is reduced after the fault is introduced and the temperature difference of the inlet an outlet temperature is increased. This is in line with the rule 20. Therefore, through expert rules 16–20, the EXV stuck at intermediate position fault of IDU can be diagnosed and the faulty IDU can be identified.

which is caused by the compressor oil recovery operation. Figure (b) demonstrates the change trend of the temperature difference between inlet and outlet pipes. In the case of IDU fouling fault condition, the temperature difference between the inlet and outlet pipes is very small or even below zero, in line with rule 21. The changes of the outlet temperatures of fouling IDU and normal IDU are further analyzed as shown in figure (c). The outlet temperature of the fouling IDU is lower than that of the normal IDU, which is in line with the rule 22. Therefore, for the IDU fouling fault, the IDU parameters can be used to diagnose the fouling fault and identify the faulty IDU when the ODU parameters lost the fault diagnosis ability. 4.6. Overall fault diagnosis performance Through Sections 4.2–4.5, the performance of the VFDR is evaluated by typical faults. The results show that the proposed fault diagnosis strategy can effectively diagnose faults of the VRF system. However, the strategy can only judge whether the rule is met by analyzing the trend of change and cannot quantify the fault diagnosis performance. Therefore, this paper proposes DCR to evaluate the faults of VRF system. The fault diagnosis results are listed in Table 4. There are two cases of judging the effect of fault diagnosis by using DCR. One is that the fault corresponds to only one rule, so the DCR corresponding to this is the correct rate of fault diagnosis. Another case is that the fault corresponds to multiple rules, and the fault has a corresponding DCR of all the rules. For example, the overall DCR of rules 9–12 corresponding to refrigerant undercharge, which is the overall correct rate of this fault. The detailed calculation method can be used Eq. (7). According to the

4.5. The IDU fouling fault under cooling mode Fig. 11 presents the results of the IDU fouling fault under cooling mode. The IDU 4 is artificially introduced the fouling fault. As can be seen from the figure (a), the inlet and outlet pipe refrigerant temperatures of the fouling IDU are very close. Even at some point, the outlet temperature of the fouling IDU is equal to or less than the inlet temperature. This indicates that the heat exchange capacity between the fouling IDU and indoor air is reduced. The part marked by the red circle shows the fluctuation of the inlet and outlet temperatures of the IDU,

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Table 4 Fault diagnosis performance of VFDR strategy under cooling mode. Fault type

Mode

Rule

Fault diagnosis rate

The number of samples

Tcom, dis sensor bias

Cooling System shutdown Cooling Cooling

Rule2 Rule3

0.9701 0.9947

536 377

Rule1 Rule5

0.7652 0.9904

115 830

Cooling Cooling Cooling Cooling Cooling Cooling Cooling Cooling Cooling Cooling Cooling Cooling Cooling Cooling Cooling Cooling Cooling & IDU4 fault Cooling & IDU4 fault Cooling & IDU4 fault Cooling & IDU5 fault Cooling & IDU5 fault Cooling & IDU5 fault

Rule7 Rule9 Rule10 Rule11 Rule12 Rule9–12 Rule13 Rule14 Rule15 Rule13–15 Rule16 Rule17 Rule18 Rule19 Rule20 Rule16–20 Rule21

0.9772 1.00 0.9731 0.8581 0.9872 0.8478 0.9181 0.7047 0.9371 0.7047 0.9787 0.9761 0.9840 0.9787 0.9309 0.9309 0.8696

569 782 782 782 782 782 1209 1209 1209 1209 376 376 376 376 376 376 3543

Rule22

0.8905

3543

Rule21–22

0.8470

3543

Rule21

0.8425

3543

Rule22

0.9277

3543

Rule21–22

0.8225

3543

Tcom, dis sensor detached Tspr , in sensor bias Tspr , in sensor detached

Tod sensor bias Refrigerant undercharge

EXV stuck closed fault

EXV stuck at intermediate position fault

Indoor unit fouling

Fig. 11. The IDU fouling fault under cooling mode.

Cooling

Rule4

0.8132

744

diagnosis performance is not satisfactory for the slight bias of the sensor. For refrigerant charge faults, the rules presented in this paper can diagnose 63% refrigerant charge level fault. For the case that the refrigerant charge level is less than 63%, the fault symptom is more obvious because of the increase of the fault severity and can be diagnosed using proposed rules. However, when the refrigerant charge level is higher than 63%, the performance of fault diagnosis based on rules needs further verification. The VFDR strategy proposed in this paper is developed based on the VRF system which has an outdoor unit and five indoor units, and verified by experimental data. When the type of the VRF system is very different, the relevant rules need to be modified accordingly to apply.

result of fault diagnosis, the DCRs of all faults are higher than 70%, and most DCRs are higher than 80%. The worst fault diagnosis performance is EXV stuck closed fault, whose DCR is 70.47%. Besides, the overall correct rate of all fault is 85.13%. The VFDR strategy proposed in this paper can effectively diagnose three types of faults in VRF system. The rules obtained in this study are only validated when the single fault occurs. When multiple faults occur at the same time, they will have mutual influence and the fault characterization will change. Whether the rule can be applied requires further verification. 5. Conclusions

Acknowledgment

This paper presents a VFDR fault diagnosis strategy based on expert knowledge. A total of 22 expert rules are established, of which 10 rules for the ODU and 12 rules for the IDU. The actual VRF system has been investigated to introduce three types of faults under cooling mode, a total of nine faults. Nine faults of the VRF system are well diagnosed by the fault diagnosis strategy based on expert rules. Especially for IDU faults, not only can the fault be diagnosed, but the faulty IDU can also be identified. The DCRs of all faults exceed 70%. Among them, EXV stuck closed and Tcom, dis sensor detached faults are diagnosed with DCRs of 70.47% and 76.52% respectively. Except for these two faults, DCRs of other faults exceed 80%. Through expert rules fault diagnosis strategy, the overall DCR of all faults is 85.13%, which indicates that the VFDR strategy proposed in this paper are effective and applicable for the fault diagnosis of VRF system. The sensor fault diagnosis rules proposed in this paper are only suitable for the case when the sensor bias fault is serious, and the fault

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