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Article Excerpt
INTRODUCTION
The development of fault detection and diagnosis (FDD) methods for HVAC applications has been an area that has been actively researched for more than a decade. Numerous studies report methods developed for application to central air-handling units (AHUs) based on either passive monitoring, where data are collected without interrupting the normal operation of the system (e.g., Haves et al. 1996; Dexter and Benouarets 1996; Lee et al. 1996a, 1996b, and 1997; Katipamula et al. 1999; House et al. 1999 and 2001; Norford et al. 2002), or active testing, where data are collected that result from overrides of control signals and setpoints (e.g., Kelso and Wright 2005; Xu et al. 2005; Haves et al. 2007; Katipamula and Brambley 2007). Katipamula and Brambley (2005a and 2005b) provide a review of the literature on the topic of FDD methods for HVAC applications published prior to 2005.
The development and implementation of an FDD method involves a trade-off between the sensitivity of the method to faults and the number of false alarms that it will generate (Reddy 2007; Katipamula and Brambley 2007). The development of robust FDD methods for the HVAC industry is challenged by the fact that there are limited sensors available for use in analyses (due to cost considerations, only those necessary to control the equipment are commonly installed), the equipment and systems have nonlinear characteristics, and the loads on the system are time varying. Methods can be made more sensitive to faults and less likely to create false alarms if the data they process are collected under well-defined and well-controlled conditions. For this reason, AHU FDD methods are commonly based on analyses of data collected while operating in steady state. For AHU FDD methods implemented as on-line monitoring tools, a filtering algorithm, commonly referred to as a steady-state detector, is often used to collect data while the system operates in steady state and to discard data from transient operation (e.g., Haves et al. 1996; House et al. 1999 and 2001). For AHU FDD methods implemented as commissioning tools (i.e., those that utilize active testing), steady-state data can be obtained by overriding a control signal and forcing the AHU into a particular operating state until steady-state conditions prevail (Kelso and Wright 2005; Xu et al. 2005; Haves et al. 2007; Katipamula and Brambley 2007).
Both approaches to obtaining steady-state data have drawbacks. Because unstable operation is prevalent in AHUs, a well-designed steady-state detector could discard large portions of the operational data, leaving little data for an on-line FDD method to process. FDD methods that rely on injected test signals would be used intermittently and may require an operator to either manually introduce the test signals or to monitor the test as it progresses. Thus, faults could exist for significant periods of time before they are discovered, because data are being discarded or collected infrequently.
This paper describes a simulation study of a new method for integrated control and fault detection of AHUs that overcomes these drawbacks. The method uses sensors commonly installed in AHUs and collects much of the key diagnostic information at times when steady-state conditions are imposed on the AHU by the sequencing logic, thereby eliminating the need for a steady-state detector. This enables the method to continuously monitor the AHU operation and over time produces a rich data set collected under controlled conditions. A model-based fault detection method processes these data and generates residual values that can be further processed to identify faults. In parallel, an algorithm monitors the saturation status of control loops for the processes used for sequential control of the AHU.
The paper is organized in the following manner. First, the AHU system description and finite state machine sequencing control are described. This is followed by descriptions of the integrated control and fault detection method and the simulation environment used to evaluate the method. Results obtained for six faults are then discussed in detail, and a table summarizing the results for all faults considered in the study is presented. Finally, conclusions and recommendations for future work are provided.
AHU SYSTEM DESCRIPTION
Figure 1 is a schematic diagram of a single-duct central AHU. Outdoor air enters the AHU and is mixed in the mixed air plenum with recirculated air returned from the building. The supply fan draws mixed air through the heating and cooling coils where it is conditioned, if necessary, prior to being distributed to the building through the supply duct. Return air from the building is either exhausted or recirculated to mix with outdoor air. The outdoor, recirculation, and relief airflow rates are controlled by their respective dampers (collectively called the mixing-box dampers) and by the supply and return fans.
[FIGURE 1 OMITTED]
In variable-air-volume (VAV) AHUs, the supply air temperature is commonly controlled to satisfy a setpoint value. Feedback control is used to modulate the heating coil valve, cooling coil valve, and mixing-box dampers to achieve the setpoint. An AHU controller uses sequencing control logic to determine the proper component(s) to use to control the temperature at any given time. Seem et al. (1999) and ASHRAE (2007) describe a sequencing strategy for AHUs based on finite state machine logic. A state transition diagram illustrating the logic for the sequencing strategy is shown in Figure 2. The description of each operating state is summarized in the rounded boxes and described further below. The conditions necessary for transitions between states are provided adjacent to the arrows connecting the states.
[FIGURE 2 OMITTED]
State 1
In State 1, feedback control is used to modulate the amount of energy transferred from the heating coil to the air. The mixing-box dampers are positioned to provide the minimum outdoor airflow rate required for ventilation and the cooling coil valve is closed. The transition to State 2 occurs after the control signal has saturated in the no-heating position (i.e., closed). The control signal is considered saturated in the no-heating position when it has been continuously at this position for a time period equal to the state transition delay. A state transition delay of five minutes was used in this study.
State 2
In State 2, feedback control is used to modulate the mixing-box dampers in order to maintain the supply air temperature at the setpoint value. Adjusting the positions of the dampers varies the relative amounts of outdoor air and return air in the supply airstream. In State 2, the heating and cooling coil valves are closed. The transition to State 1 occurs after the control signal for the dampers has been at the minimum outdoor air position for a time period equal to the state transition delay. Transition to State 3 occurs after the control signal for the dampers has been at the 100% outdoor air position for a time period equal to the state transition delay.
State 3
In State 3, feedback control is used to modulate the flow of chilled water to the cooling coil, thereby controlling the amount of energy extracted from the air. The mixing-box dampers are positioned for 100% outdoor air, and the heating coil valve is closed. Transition to State 2 occurs after the control signal for mechanical cooling has been saturated at the no-cooling position for a time period equal to the state transition delay. Economizer logic is used to determine the transition to State 4. Enthalpy-based, temperature-based or combined enthalpy and temperature economizer logic may be used. In the state transition diagram shown in Figure 2, logic based on the outdoor air temperature is used to determine the transition point. Transition to State 4 occurs when the outdoor air temperature is greater than the switchover temperature plus the deadband temperature. Typically, the switchover temperature is equal to the return air temperature, and the deadband is about 0.56[degrees]C (1[degrees]F). The deadband prevents cycling from State 3 to State 4 caused by noise in the return and outdoor air temperature sensor readings.
State 4
State 4 also uses feedback control to modulate the flow of chilled water to the cooling coil, thereby controlling the amount of energy extracted from the air. However, in this case, the mixing-box dampers are set at the minimum outdoor air position. Economizer logic is used to determine the transition to State 3. In the state transition diagram shown in Figure 2, transition to State 3 occurs when the outdoor air temperature is less than the switchover temperature.
INTEGRATED CONTROL AND FAULT DETECTION SYSTEM
FDD methods can be classified as either model-free methods or model-based methods (Gertler 1998). Model-free methods include methods based on: 1) physical redundancy, in which multiple sensors are installed to measure the same physical quantity; 2) special sensors installed to specifically detect and diagnose particular faults; 3) limit checking, in which process variables are compared to thresholds; 4) spectrum analysis to detect and identify faults in rotating machinery; and 5) logic reasoning approaches. As the name implies, model-based methods use a model of a process to calculate expected values of specific variables. The expected values are compared to measured values and the differences, or residuals, are evaluated to determine if a fault exists.
The overall structure of the integrated control and fault detection system is shown in the block diagram in Figure 3. A finite state machine is used to provide sequential control of the devices. Based on the current state or state transition, observations are passed from the finite state machine to the model-based residual generation block. This block determines residuals based on mass and energy balances of the system. Within the finite state machine, a control performance monitor calculates state-based performance indices for the control...
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