ASHRAE OR-05-13-3-2005 Developing Component Models for Automated Functional Testing《为自动化功能测试而开发组件模型》.pdf
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1、OR-05-1 3-3 Developing Component Models for Automated Functional Testing Richard M. Kelso, PhD, PE Fellow ASHRAE ABSTRACT Reference models developed from first principles and empirical relationships are used to represent correct operation of air-handling units. The models are incorporated into soft-
2、 ware capable of comparing actual system output measure- ments with model outputs and detecting deviations from correct operation. Tests of the model-based system with data from a real system, operating with and without introduced faults, are reported. ROLE OF COMPONENT MODELS IN AUTOMATED FUNCTIONA
3、L TESTING This paper presents the development of reference models for use in automated functional testing during commissioning of building HVAC systems. A companion paper (Kelso and Wright 2005) discusses the concepts of model-based auto- mated testing. Model-based fault detection and diagnosis uses
4、 reference models of the system or components to provide analytic redundancy. Values of output variables read from the system are compared with reference values predicted by the models. Differences between the two, or errors, are indicators for detection of faulty operation (Figure 1). Neural networ
5、k (“black box”) models have been applied to this task, but they require training on correctly operating systems and are thus limited to continuous commissioning rather than initial functional testing. The models chosen for this investigation were based on first principles or empirical relationships.
6、 The variables represent values that can be chosen from design intent information and do not require that the system be operating correctly. Jonathan A. Wright, PhD Member ASHRAE The model equations chosen are algebraic, nonlinear, deterministic, and discrete. The thermodynamic relationships from wh
7、ich the models are derived are valid for steady-state conditions, and the models are therefore constrained by this limitation. A quasi-dynamic first-order model is considered below. The model inputs are state variables measured by the digital HVAC control system. The parameters are variables related
8、 to physical characteristics of the components and are constant for a selected component. The parameter values form the links that convert the general component model to a specific model of a component in the system to be tested. The models must have parameters that (1) are specifically indica- tive
9、 of certain fault conditions and (2) have values that are readily available from construction documents, manufac- Control Inputs I ParYeters i + AL In uts Figure I Information flow diagram for reference models. Richard M. Kelso is a professor at the University of Tennessee, Knoxville, Tenn. Jonathan
10、 A. Wright is a senior lecturer at Loughborough University, Loughborough, Leicestershire, UK. 02005 ASHRAE. 971 turers literature, or other engineering design intent informa- tion. The output variables, or variables, are state variables that can be compared to measured quantities for fault detection
11、. It is essential that the models be able to represent the full range of operating conditions that may be encountered, since it is not feasible to wait for design conditions to test the systems. Part-load conditions are likely, and the models must be able to extrapolate to design conditions from the
12、 test condi- tions. The models should be as simple and easy to understand as possible. There must be parameters to represent control characteristics such as leakage, nonlinearity, and hysteresis. Models intended to represent correct operation almost always have some degree of divergence from the per
13、formance of the real system. For these reasons, the automated commis- sioning process must include some information about the degree of confidence the user can have in the truth of an outcome. The tool must minimize false-positives (false alarms) yet not be so tolerant that only catastrophic failure
14、s are detected. The issue is to understand the degree of uncertainty due to the structure of the model as distinct from the uncer- tainty due to that in the input variables and the parameters. Signals from digital control systems are not continuous, but discrete. The HVAC control system typically se
15、nds and receives signals between its various sensors, controllers, and actuators at a rate of fractions of a second. Because of the normally slow rate of change in an HVAC system, intervals between signals extracted from the control system and used in FDD work are on the order of one minute or more.
16、 An interval of one minute is used here. The signals can be considered deterministic, since instrument noise is of far higher frequency and random inputs are not present. Uncertainties must be accounted for, however. The system can be represented by the vector of n compo- nents: Parameter 1. Coil wi
17、dth ri Value Parameter Value 0.9 m (36 in.) 2. Coil height 0.6 m (23 in.) Components in the system can be modeled by 3. Number of rows 5. Tube internal diameter 7. Valve leakage where y represents the state outputs, x the state inputs and u the control signals, both of which are functions of time, a
18、ndp the parameters. For a fault to be detectable and distinguishable from the uncertainties, the component equations must be in a form that includes the uncertainty. Diagnosis can follow detection of a fault condition. Two methods have been identified. One is to apply an optimization procedure so th
19、e model output variables match the faulty system outputs. Changes in the parameters required to make the outputs match indicate the faults. Faults can also be diag- nosed by a set of expert rules. Each component can be excited by a series of control inputs, and a fault can be isolated to the selecte
20、d component by testing each component in series while progressing downstream along the air path. Testing each component in turn simplifies the expert rules. 2 4. Number of circuits 18 0.012 m(0.47 in.) 6. Valve curvature 2.95 0.0 8. Valve authoritv 0.64 EXPRESSING DESIGN INTENT WITH MODELS 9. Valve
21、hysteresis The model-based functional testing concept described in this paper was applied in a testing program utilizing real air- handling units at the Iowa Energy Centers Energy Resource Station (IEC ERS). Examples of two of the models used are described in some detail, and results of tests of cor
22、rect and faulty operation are presented. As an illustration of the role and derivation of the model parameters, the heating coil parameters will be examined in detail. The parameters are listed in Table 1. Of these parameters, values for numbers 8, 10, and 15 were found in the construction drawings;
23、 1,2, 3, and 6 were obtained from manufacturers submittal data; and 4 and 5 required direct inquiry to the manufacturer. Number 7 is a logi- cal design intent, and number 9 is a realistic acceptance of typical commercial performance. Numbers 1 1 - 14 were taken 0.14 10. Water maximum flow 1.3 kgls (
24、2lgPm) Table 1. Heating Coil Parameters 1 1. Air side resistance 1.1(6.24) 12. Metal resistance 0.38 (2.15) 13. Water side resistance 15. Maximum duty 0.22 (1.25) 14. UA scale 1 .o 61KW (208MBH) 16. Convergence tolerance 0.0005 972 ASHRAE Transactions: Symposia Temperature Leaving First order dynami
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