Research on the Influence of Valve Sensor Fault Based on Simulation

: In the practical application of indoor air quality assurance system, it is difficult to detect the fault of air valves, CO2 sensors and other components, resulting in substandard air quality and increased energy consumption. In this paper, 16 fault conditions and 1 no fault condition were simulated by Simulink simulation software for primary return air conditioning system in summer, and a typical indoor air quality assurance system simulation fault characteristic table was obtained. It is found that PM2.5 sensor fault mainly affects indoor PM2.5 purification speed. The fault of a CO2 sensor affects indoor CO2 dilution and system energy consumption. Small resistance of return air valve increases system energy consumption. Small resistance of supply air valve, exhaust air valve and return air valve decreases the system's ability to control indoor pollutants.


Abstract:
In the practical application of indoor air quality assurance system, it is difficult to detect the fault of air valves, CO2 sensors and other components, resulting in substandard air quality and increased energy consumption. In this paper, 16 fault conditions and 1 no fault condition were simulated by Simulink simulation software for primary return air conditioning system in summer, and a typical indoor air quality assurance system simulation fault characteristic table was obtained. It is found that PM2.5 sensor fault mainly affects indoor PM2.5 purification speed. The fault of a CO2 sensor affects indoor CO2 dilution and system energy consumption. Small resistance of return air valve increases system energy consumption. Small resistance of supply air valve, exhaust air valve and return air valve decreases the system's ability to control indoor pollutants.

1.Introduction
In recent years, with the improvement of living standards, people have paid more and more attention to the problem of air pollution [1,2] . In order to effectively maintain good indoor air quality, more and more people use sensors to monitor various indoor air parameters. Sensors can accurately and objectively monitor indoor air quality, so as to guide the HVAC system to carry out purposeful regulation of indoor air. However, in the process of regulation and control, if the sensor as the control basis or the valve as the control means fails, it will inevitably affect the energy consumption of the air conditioning system and indoor air quality.
Since the beginning of last century, there have been researches on fault diagnosis of indoor air quality assurance system at home and abroad. Du [3] proposed a fault diagnosis method for air conditioning system sensors. Faults include temperature, flow rate and pressure, and the fixed deviation of temperature sensor is ±10% and ±8%. The fixed deviation of flow sensor is ±10% and the drift slope is ±0.2kg/s/h; Pressure sensor deviation of 20% and drift slope of 30Pa/h. Ying Yan [4] proposed a fault diagnosis method based on distributed Boltzmann machine to realize the offset and drift fault diagnosis of sensors of air supply temperature, air mixing temperature, air supply volume and air return volume of air conditioner. Shiqiang Wang [5] proposed a distributed genetic algorithm, which can realize the fault detection and self-correction of pressure, flow and temperature sensors in distributed HVAC. Woo Seung Yun [6] proposed a data-driven fault diagnosis method for air processor, realizing the fault diagnosis of fresh air valve blockage, exhaust valve blockage, water system valve blockage and instability, heating coil surface scaling fault, and used ASHRAE RP-1312 experimental data to test the effect. Many scholars at home and abroad use MATLAB Simulink as a tool for modeling in the research process, and obtain the data set of the operating parameters of the fault system under the fault state through simulation, and build the data diagnosis method. Shaoyong Li [7] used MATLAB Simulink to model the fan coil system and used this model to complete the PID control optimization research of indoor temperature based on ant colony optimization algorithm, so as to improve the quality of indoor temperature control. Tom Marsik [8] used MATLAB Simulink to model an airconditioned room equipped with a heat recovery device with particulate matter filtration function, and conducted simulation with the model, which proved that the device played a positive role in reducing energy consumption and improving indoor air quality.
As a simulation tool, MATLAB Simulink has been widely used in air conditioning systems. Many researchers at home and abroad have used Simulink as a tool to build reliable air conditioning system models, but they have not conducted modeling and simulation for buildings with indoor air quality assurance systems that take into account CO 2 and PM2.5 pollutants. Therefore, this paper uses MATLAB Simulink software to build a typical indoor air quality assurance system model. According to the common faults of indoor air quality assurance system obtained in literature research, the fault conditions are set, and the characteristic parameters of system operation are selected to analyze the influence of each fault on the CO2 dilution, PM2.5 purification and energy consumption performance of the system.

Research Method
According to the literature research, the simulation experiment condition table was designed. The relevant parameters of the simulation room were determined, and the mathematical model of the main body, control and energy consumption system of the air quality assurance system was established. The simulation model of the typical indoor air quality assurance system was built by Simulink simulation software, and the fault and fault free conditions were simulated and studied. The variation of transport characteristic parameters in each working condition is analyzed.

Research Index
Indoor air quality assurance system mainly focuses on indoor pollutants such as CO 2 , particulate matter and VOCs, among which CO 2 and VOCs pollution sources are indoor, and the control means are mainly diluted by outdoor fresh air. Meanwhile, some existing studies [9,10] show that the reliability of low-cost VOCs sensors still needs to be improved. Therefore, CO 2 is taken as the representative of the research; Generally, PM10 and PM2.5 are used to limit the concentration of particulate matter in indoor air. Here, PM2.5 with smaller particle size is selected as the research index. The fault may also affect the system energy consumption. If the fresh air valve is stuck, the system needs more load for hot and wet treatment of the fresh air. Therefore, this paper takes CO 2 concentration, PM2.5 concentration and energy consumption as research indicators to extract characteristic parameters of system operation, so as to describe the impact of faults on the system.

Model Building
After the mathematical model is established by Simulink tool, the calculation results can be simulated quickly and a large number of parameters of each point can be obtained for calculation and analysis, which is suitable for this study. Therefore, Simulink simulation software was used to build the simulation model of typical indoor air quality assurance system.
Simulink simulation requires deducing and establishing the relevant mathematical model, and then completing the construction of the simulation model according to the mathematical model. The mathematical model of the main part of indoor air quality assurance system is divided into fan system providing power, valve and conveying system regulating fresh air ratio, air filtration device and pollutant source system. Control system for valve and fan gear regulator. The energy consumption of indoor air quality assurance system is divided into fresh air energy consumption and fan energy consumption.
The main part of the typical indoor air quality assurance system is composed of air ducts, pipe fittings, valves and fans, indoor CO 2 purification module with pollution sources and purification and dilution mechanism, indoor PM2.5 purification module. The control system is composed of air duct parameter input module, fan controller, valve controller, fan speed regulation and valve opening signal conversion module. The energy consumption system consists of energy consumption modules, as shown in Fig. 1.   Fig. 1. Simulation model diagram

Modeling Scheme
This study selects a typical simulated room: an office room 3 meters long, 4 meters wide and 3 meters high. There are 3 office workers in the room, and the air supply is 180m³/h according to engineering experience. The concentration of CO 2 in the outdoor atmosphere is set to 400ppm, and the initial concentration of CO 2 is set to the standard limit of 1000ppm. The initial PM2.5 concentration is set to 500μg/m³ and the outdoor atmospheric PM2.5 concentration is set to 75μg/m³. In order to ensure the rapid control of indoor pollutants in the purification mode, a double-fan single-return air system is adopted.
The common fault is valve block. In view of this fault, simulated valve fault conditions are set for fresh air valve, return air valve, exhaust valve and supply air valve. The common faults of sensors are offset, drift and reduced accuracy. For the CO 2 sensor and PM2.5 concentration sensor of indoor air quality assurance system, set the fault condition of the simulated sensor. For specific design, as shown in Table 1: The accuracy of PM2.5 sensor is reduced A To simplify the form, replace "Pollutant concentration control" with symbol "A"

Findings and Analysis
The diluting speed ratio (A vx ), purifying speed ratio (A vj ), fan two-stage running time ratio (A t ), fan energy consumption ratio (A f ), fresh air energy consumption ratio (A x ) and system energy consumption ratio (A w ) were selected to describe the fault operation characteristics of indoor air quality assurance system.

The Effect to CO2 Dilution of each Fault
In addition to indoor PM2.5 sensor fault, valve jamming fault and indoor CO 2 sensor fault will affect the CO 2 dilution effect. From the range distribution of indoor CO 2 concentration, smaller supply valve and larger return valve will increase the range of indoor CO 2 concentration, while smaller exhaust valve and smaller CO 2 sensor will decrease the range of indoor CO 2 concentration. From the perspective of system control ability, larger supply valve, larger fresh air valve and smaller return air valve will lead to enhanced system control ability, while smaller exhaust valve will lead to weakened system control ability, and the degree of enhancement or weakening is different in each working condition. When the exhaust valve is too small and the CO 2 sensor is too large, the system cannot complete indoor CO 2 dilution, and the A VX in fault condition fluctuates between 72.2% and 162.7%, as shown in Table 2. 100.0 -No significant change Note: The data presented in percentage mode in the table is the ratio between the data in the current working condition and that in standard working condition. "-" indicates that the parameter is not affected by faults in the current working condition.

The Effect to PM2.5 Purification of each Fault
Except for the large blockage fault of the return air valve, all the faults will affect the purification effect of PM2.5. From the perspective of system control ability, smaller exhaust valve, smaller return air valve, smaller PM2.5 sensor, smaller PM2.5 sensor drift will reduce the system control ability, and the exhaust valve blockage small fault system control ability decreased the most, reduced to 56.5%. Large supply valve, large fresh air valve, large CO 2 sensor, and large CO 2 sensor drift increase the system regulation ability. Among them, large supply valve blockage increases the system regulation ability the most, rising to 125.3%. A vj in fault condition fluctuates in the range of 56.5% to 125.3%, as shown in Table 3 and Fig. 2. The data presented in percentage mode in the table is the ratio between the data in the current working condition and that in standard working condition. "-" indicates that the parameter is not affected by faults in the current working condition.

The Effect to System Energy Consumption of each Fault
Except for the fault of indoor PM2.5 sensor precision reduction, other faults will affect energy consumption factors. Indoor PM2.5 sensor failure mainly affects the running time of the second gear fan. When the indoor CO 2 sensor drift is small fault, the system energy consumption is reduced to about 58%. When the indoor CO 2 sensor has a large fault, the system energy consumption increases the most, rising to about 234%. A w , A x , A f and A t fluctuates in the range of 57.9%-235.2%, 47.5%-261.6%, 90.3%-134.0% and 35.8%-338.6% respectively under the fault condition, as shown in Table 4. Note: The data presented in percentage mode in the table is the ratio between the data in the current working condition and that in standard working condition.

Conclusion
In this paper, Simulink simulation software was used to design and build a typical indoor air quality assurance system model of a three-person office with a size of 3m×4m×3m. In summer, 16 fault conditions and 1 fault free condition related to indoor PM2.5 sensor, indoor CO 2 sensor and air supply valve, fresh air valve, exhaust valve and return air valve were simulated. The following conclusions are reached: 1) Sensor fault: PM2.5 sensor failure mainly affects indoor PM2.5 purification speed, but has no obvious effect on other parameters. In addition to affecting the dilution of indoor CO 2 , the failure of CO 2 sensors also has a great impact on the system energy consumption. Large indoor CO 2 sensors and large indoor CO 2 sensor drift can increase the system energy consumption by 35.2% in summer. However, small indoor CO 2 sensors and smaller indoor CO 2 sensor drift will stabilize indoor CO 2 concentration above the limit value and deteriorate indoor air quality. At the same time, valve gear changes caused by indoor CO 2 sensor failure will also have a certain impact on the purification speed of PM2.5.
2) Valve failure: small return air valve blockage increases system energy consumption by 27.4% in summer; Small blockage of supply air valve, exhaust valve and return air valve reduce the regulation ability of indoor pollutants, reducing the purification capacity of PM2.5 by up to 43.5%. The reduction of CO 2 dilution capacity can make the system unable to complete the regulation of indoor CO 2 .
3) Due to the deficiency of modeling with only a single pollutant as the target in the current research on indoor air quality assurance system, this paper builds a set of simulation model for the whole indoor air quality assurance system, including room, heat and cold source, fan, air duct, valve, sensor, pollution source and control means, to provide help for subsequent research.