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Fault evaluation process in HVAC system for decision making of how to respond to system faults

One of the effective methods for energy conservation in buildings is to operate the building system with high efficiency and to reduce the waste of power consumption. However, buildings are often considered to contain faults, and a fault is one of the causes of hindering high-efficiency operation. Therefore, in a maintenance plan for building systems, it is necessary process that to detect and diagnose faults. In addition, an operation and maintenance process of building systems using fault detection and diagnosis (FDD) essentially requires evaluating the impacts of faults in order to decide how to respond to faults. However, many studies on FDD have not considered a fault evaluating process. Here we focused on faults in a real heat source system and evaluate the impact of faults and determine the priority to repair faults in order to develop an appropriate maintenance plan of the system. We developed a detailed simulation model of the system combining automatic control system based on the specifications. Using this simulation, we calculated the system behavior without faults and behaviors with thirty-five faults. We evaluated the yearly impact of the faults using the system coefficient of performance (SCOP) and the peak power as indicators. In addition, using the actual FDD results in previous studies, we also evaluated faults in consideration of the actual daily impacts of them. We found that there were differences in the priority depending on the indicators. we expected that this fault evaluation method helps an operation and maintenance of the system.


Introduction
One of the effective methods for energy conservation in buildings is to operate the building system with high efficiency and to reduce the waste of power consumption. However, buildings are often considered to contain faults, and a fault is one of the causes of hindering high-efficiency operation. In commercial buildings which of systems are not properly managed and optimally controlled, an estimated 5% to 30% of the energy is wasted [1] [2]. Therefore, in a maintenance plan for building systems, it is necessary process that to detect and diagnose faults and to decide how to respond to them appropriately. Many studies on fault detection and diagnosis (FDD) in a building's operation have been carried out. On the other hand, many studies on FDD have not considered a process of deciding response to faults appropriately. In order to decide appropriate response to faults, an operation and maintenance plan of building systems using FDD essentially requires evaluating the impact of faults [1]. Here we evaluate the impact of faults and determine the priority of faults to repair in order to develop an appropriate maintenance plan of the system. In previous studies on the impact evaluating of faults, the yearly impact of each fault on a country unit was calculated using actual measurement data of target buildings [3] [4]. However, the method cannot be used for examination of daily impacts in specific systems. Therefore, in this study, we focused on a specific heat source system and evaluated the yearly and daily impact of faults in the system. First, we developed detailed simulation model of a real heat source system. Based on it, we calculated faulty system behaviours. Using these results, we evaluated the yearly impact of the faults using the system coefficient of performance (SCOP) and the peak power as indicators. In addition, using the actual FDD results in previous studies [5], we also evaluated faults in consideration of the actual daily impacts of them.

Target system
In this study, we focused on a heat source system of a real building. The building is a factory completed in 2003. Specifications of equipment in the target system and a conceptual diagram of the system are shown in table 1 and figure 1. This system consists of centrifugal Liquid chiller, primary chilled water pump, secondary chilled water pump, condenser water pump, direct-contact cooling tower. In this system, a control of the number of chillers and secondary chilled water pumps, variable flow rate control on primary chilled water pumps, secondary chilled water pumps and condenser water pumps, condenser water bypass valve control and variable air volume control on cooling towers is performed. Actual operation data were collected by the building energy management system (BEMS) at 15-minute intervals and in this study the data of 2013 and 2014 was used.

Simulation model
In order to quantify the impact of faults in the target system, we developed a simulation model that can calculate the behaviour of the system. Using this model, we can calculate the system behaviour as designed based on the specifications of equipment and control of the target system. This simulation model was developed by combining automatic control logics based on the specifications and control with the physical models of the equipment. The calculation flow of the model is shown in figure 2. It is performed in the order of automatic control, calculation of flow, calculation of heat, and calculation of power consumption. Five input values are used from the BEMS data: set value of supply chilled water flow rate, a load heat quantity, a set point of supply water temperature, an outside air drybulb temperature, an outside air relative humidity. Finally, this model outputs 50 items such as a flow and temperature of chilled water and condenser water, a power of equipment, and so on. In this model, the calculation period is one year, and the calculation interval is one minute by performing linear interpolation on input values. In this study, we calculated by using the BEMS data of 2013 and 2014 as input values.

Data generation of faulty system behaviour
By using this simulation model, we calculated the system behaviours without faults and with each fault. These behaviours are calculated by incorporating a fault into the simulation model which can calculate the system behaviour without faults. We assumed 35 types of faults shown in table 2 in order to cover a wide range of faults occurring in the system. For each fault, we calculated the system behaviours for two years in 2013 and 2014. In the following fault impact evaluations, we used these calculation results.

Results
We evaluated the impact of faults using the calculation results in 2.3. In this study, in order to evaluate the impact of faults, two indicators were used: the SCOP and the peak power. The peak power is a criterion for determining the contract power and the electricity charge. Therefore, the peak power is also an important indicator in the impact evaluation of faults. For each indicator, we quantified the impact of each fault on the system and compared them. Considering cases where the interval of maintenance is long or short, we calculated the yearly and daily impact of faults in the system.

Evaluation of yearly impact of faults
Using the calculation result of 2014, we calculated the yearly impact of faults on each indicator. In the case of using the SCOP as an indicator, from the annual SCOP of system behaviour without faults (F0) and with each fault (F1 to F35), the change rate of annual SCOP was calculated as equation (1).
where Fn is a fault label, , is a rate of change of annual SCOP due to is the annual SCOP of Fn .
From these results, we ranked the faults in descending order of the annual SCOP decreasing rate. The results are shown in table 3(a). The change rate of annual SCOP was large in F5 and F1. Analysing these faults, it was found that both had the impact of increasing the power consumption of the chiller. In addition, the impact of annual SCOP due to a fault related to condenser water was significant. In the case of using the peak power as an indicator, from the annual peak power of system behavior without faults (F0) and with each fault (F1 to F35), we calculated the change rate of annual peak power as equation (2). From these results, we ranked the faults in descending order of the increasing rate of annual peak power. The results are shown in table 3(b). In the case of using the peak power as an indicator, the impacts of F16 and F26 were significant, and it was different from the impact of the annual SCOP. In addition, the impact of annual peak power due to a fault related to chilled water was significant.

Evaluation of daily impact of faults
In the analysis on daily impacts of faults, a calculation of fault impacts was taking into consideration the occurrence probability of each fault. In this study, we used the diagnosis probability obtained from FDD result of this system [5]. The figure  3 shows the calculation result of diagnosis probability in 2014. In a method of calculating the probability of diagnosis, all 35 types of fault data shown in the table 2 were used as training data and test data.   The set point of condenser water outlet temperature of cooling towers rises by 10% of the rated value (0.5 degree).

F25
The set point of condenser water outlet temperature of cooling towers drops by 10% of the rated value (0.5 degree).       example, on January 1st, F10, F18, F35 were occurring and the severity of them is relatively 45%, 50%,5%. In this study, we used the diagnosis probability as the relative severity of the fault. We assumed that the rate of change of SCOP and the rate of change of peak power change linearly with the severity of faults. In the case of using the daily average SCOP as an indicator, we calculated the decrease amount of the daily average SCOP due to each fault considering the relative severity equation ( where Fn is a fault label, In the case of using the daily peak power as an indicator, we calculated the increase amount of daily peak power due to each fault considering the relative severity equation (4) where Fn is a fault label,     Figure 5. the increase amount of daily peak power In both cases, the impacts of F5, F10, F18, and F19 were significant. However, there were some differences in the order of them. In addition, although F19 was detected throughout the year, the impact appeared during summer season from June to October in both cases. This is because F19 is a fault of CWP-2 which is a condenser water pump on the side of the chiller TR-2, and the chiller TR-2 are operated mainly in summer.