Fault Detection and Diagnostics for Rooftop Air Conditioners

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31 condenser air flow rate results in a heat transfer penalty that causes changes in state variables, whose direction and intensity depend on the expansion device used. For example, evaporator temperature would increase significantly for fixed orifice systems but it would be unchanged for a TXV system until the fouling became very. A condenser fouling fault develops slowly when the system is running, so it is classified to be an operational fault. Similar to condenser fouling, evaporator filter and/or coil fouling is a componentlevel fault. For a rooftop unit, a direct impact of evaporator fouling is the reduction of evaporator air flow rate. The reduction of evaporator air flow rate results in poorer heat transfer performance and causes changes of state variables, whose direction and intensity also depend on the expansion device used. For example, condenser temperature would decrease significantly for fixed orifice systems but it would be unchanged for a TXV system until the fouling became severe level. Evaporator fouling is classified as an operational fault. A liquid-line restriction fault often occurs in a dryer or a filter and can be classified as a component-level fault. This fault has a direct impact of increasing the pressure and possibly temperature difference between the inlet and outlet of the dryer or filter. The increased pressure drop also results in a series of changes in state variables, whose direction and intensity are highly dependent on the expansion device used. For example, for a system with a fixed orifice as the expansion device, a liquid-line restriction will result in a significant reduction of refrigerant flow rate, while a moderate liquid-line restriction will not result in noticeable reduction of refrigerant flow rate when a TXV is used as the expansion device. This is because a TXV, an automatic control device, can compensate for an increased pressure drop resulting from a liquid-line restriction by increasing the opening of the TXV. Filter-driers continuously absorb water and dirt and become restricted over time, so a liquid line restriction fault is classified as an operational fault. Low or high refrigerant charge is a system-level fault because it can occur anywhere and its direct impact cannot be confined to a particular location. Refrigerant overcharge only happens during service, so it is a service fault. Low refrigerant charge 31
32 has two possible causes: refrigerant is undercharged when service was done or there is a refrigerant leakage. Therefore, low charge can be a system-level operational or service fault. Since the rooftop unit is under positive gage pressure system when charged, noncondensable gases can only be introduced during service. Non-condensable gases tend to accumulate in the condenser. Its primary impact is to increase heat transfer resistance and results in high condensing pressures and temperatures. So, non-condensable gas is considered to be a component-level service fault. In summary, the characteristic of a component-level fault is that its source impact is confined to a specific location or component and all the other impacts on the system originate from this source impact. On the contrary, the source impact of a system-level fault cannot be confined to a specific location or component. Operational faults usually develop through running and occur randomly or gradually, while service faults are introduced with service. 1.2.2 Interactions As depicted in Figure 1-6, a rooftop unit can be represented as a black-box, which is driven by faults, disturbances and overall system driving conditions, including T aoc , T aie, and φ aie , and outputs overall system state variables. It is difficult to tell which factors contribute to the current operation state directly from overall state variables. The SRB method uses normal state models to predict the normal operation states according to the overall driving conditions and generates residuals to decouple the interactions between driving conditions and faults, and further uses statistical analysis to further decouple the actions from disturbances, but leaves the couplings among the different faults untouched. This is the reason why the SRB FDD methods cannot handle multiplesimultaneous faults. 32
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CALIFORNIA ENERGY COMMISSION Final
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Acknowledgements Jim Braun and Haor
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Abstract Project 2.1, Fault Detecti
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TABLE OF CONTENTS LIST OF TABLES LI
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LIST OF FIGURES Page 1 - Field Test
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The report “Description of Labora
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mounted heat pumps for heating and
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The retail stores are in Southern C
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Figure 1 - Field Test Sites Data Co
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Gibson School (Cont’d) Woodland S
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Table 1 - Data List for Modular Sch
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BUILDING TYPE: Modular School Rooms
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HEATING / AIR CONDITIONING EQUIPMEN
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Sacramento Area McDonalds PlayPlace
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Bradshaw Road (Sacramento Area) McD
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TEST INSTRUMENTATION: Tables 2 and
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Table 2 - Data List for Inland Rest
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Castro Valley (San Francisco Bay Ar
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Castro Valley McDonalds PlayPlace P
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more or less custom design, publish
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BUILDING TYPE: Retail Store ADDRESS
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TEST INSTRUMENTATION: Similar test
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• Temperature and humidity levels
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November 2001 - December/January 20
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Table of Contents 1. Introduction..
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1. Introduction All the thermodynam
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Table 1.1 Comparisons of Three Mode
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solution procedure involves the non
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independent of the moisture content
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function, f ( X , y) , if it can be
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Figure 2.1 Neural-Network Implement
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prototype was developed by using th
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3. Comparison of black-box modeling
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which is more accurate than the exp
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Similar to GRNN, RBF has very good
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Table 3.2 RMS error (Polynomial,GRN
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Table 3.4 Contrast of Black-box mod
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Polynomial plus GRNN Training Desir
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interpolation(poly+GRNN) extrapolat
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used to build the steady-state mode
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α , ρ, τ I t t s h o A t a Figur
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Temperature (F) 82 79 76 73 Condens
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0.050 0.045 Pressure = 101.3 [kPa]
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T T − T − T W = W −W −W m =
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Figure 4.8 Output of three steady-s
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will be calculated to represent the
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180 175 RMS error = 0.3984 (F) Pred
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180 175 RMS error =1.1472 (F) Predi
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51 50 RMS error=0.3860 (F) Predicte
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4.4 California field site results S
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105 100 RMS error =0.5982 (F) Predi
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Since air conditioners always cycle
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6. Conclusions and future work So f
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Lee, W., House, J.M. and Shin, D.R.
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Table of Contents 1 Introduction...
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AHU α β 2 d i EER ∆ ∆ η v T
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$ !! !! )
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Paper statistics in HVAC FDD Number
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011>" - , ?" 8?"
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+ : 6336" ,
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+ :: !!- :: !!
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6 24 122 7) 6 3++, ) . !! /011
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)
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336 F / s $ S
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N $ V v1
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Figure 3-4 2-dimensional residual d
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10 5 Normal and current operation p
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7 N Ω f N N Ω = Ω X ,
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0 + α 6 d χ ( )
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Normal operation region Residual-2
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Table 3-2 Refrigerant Leak at 20% l
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ratio dist = P F 1 P F 1 2 1 9.0
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$ , " - (
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)(( 0( 04 ) 6330" /
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Table 4-2 Polynomial plus GRNN mode
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4.2 m r,predict (kg/min) 4 3.8 3.6
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6 1)( ( ( 6 1)( ) , ( )
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- )
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? "J> *"J060 #"J083 *$"J670 #"J08<
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Table 4-17 Detected (normal, fault)
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c=1 c=10 c=20 1 0.8 Distance ratio
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$ /) $ 0111" .. !!
Page 171 and 172: Chen, Bin and Braun, J.E, 2000. Sim
Page 173 and 174: Ventilating, Air-Conditioning and R
Page 175 and 176: 1.T amb 2.T ra 3.RH ra Plant Prepro
Page 177 and 178: F) $
Page 179 and 180: +: + $ 5
Page 181 and 182: .44(' * 5 09( ( 0( F N ( M , Σ)
Page 183 and 184: Load 20% 40% 60% 80% 100% 3 4 Fault
Page 185 and 186: Load 20% 40% 60% 80% 100% 3 4 Fault
Page 187 and 188: 3 4 0 0 0 0 0.4173 0 0.0010 0 0.000
Page 189 and 190: .44(' $ () ) ) - $
Page 191 and 192: ∆P Restrictio n Level=100%* ∆P
Page 193 and 194: 2 TABLE OF CONTENTS TABLE OF CONTEN
Page 195 and 196: 4 A1.2.3 Valve Position Expression
Page 197 and 198: 6 Figure 2-10 Decoupling refrigeran
Page 199 and 200: 8 LIST OF TABLES Table 1-1 Fault di
Page 201 and 202: 10 N = Number of generated sample p
Page 203 and 204: 12 with a fixed-orifice as the expa
Page 205 and 206: 14 (residuals) should have a zero m
Page 207 and 208: 16 expected distribution of the res
Page 209 and 210: 18 operation. Another advantage is
Page 211 and 212: 20 1.1.2.1 Original SRB Fault Diagn
Page 213 and 214: 22 Corresponding to the SRB fault d
Page 215 and 216: 24 integration of the probability d
Page 217 and 218: 26 1. Simplifies fault detection fr
Page 219 and 220: 28 Step i Do FDD on fault i . After
Page 221: 30 ROOFTOP UNIT FAULTS COMPONENT-LE
Page 225 and 226: 34 1.2.3 Decoupling of Component Fa
Page 227 and 228: 36 1.2.3.2 Condenser-Related Faults
Page 229 and 230: 38 mixture, χ ref is known as the
Page 231 and 232: 40 Table 1-3 also lists the refrige
Page 233 and 234: 42 Refrigerant Property T cond, pre
Page 235 and 236: 44 as an independent feature only f
Page 237 and 238: 46 evaporator air flow rate reducti
Page 239 and 240: 48 5. 2 Pll ∆ deviates drasticall
Page 241 and 242: 50 System-Level Faults RefUnder Ref
Page 243 and 244: 52 1.2.5 Summary of Decoupling Sche
Page 245 and 246: 54 noise, system disturbances and m
Page 247 and 248: 56 compressor data. When there is a
Page 249 and 250: 58 Figure 2-5 Decoupling evaporator
Page 251 and 252: 60 Total Pressure Drop of Liquid- L
Page 253 and 254: 62 Figure 2-11 Illustration of Demo
Page 255 and 256: 64 bypass the compressor. At this t
Page 257 and 258: 66 for T dis because there is no ov
Page 259 and 260: 68 Figure 2-14 Outputs of the FDD d
Page 261 and 262: 70 the system requires more refrige
Page 263 and 264: 72 recommended that: the system req
Page 265 and 266: 74 Figure 2-20 Outputs of the FDD d
Page 267 and 268: 76 Figure 2-21 Histogram bar plot o
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82 2.3.2 Summarized Results for Oth
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84 3 CONCLUSIONS AND RECOMMENDATION
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86 Breuker, M.S. and Braun, J. E.,
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88 Lee, W., House, J.M. and Shin, D
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90 APPENDIX 1 PHYSICAL MODELS OF EX
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92 A1.1.2 Short-Tube Models Many re
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94 m& = CA ρ P up − P ) (A1-5) (
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96 The abrupt change of CA for the
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98 These equations can be combined
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100 H h T sh , max opening T sh , s
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102 A1.2.5 Parameter Estimation Met
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104 T (2 T sh, ratingopening , sh,m
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106 Harms’Result Harms plotted al
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108 Mass flow rate Local linearizat
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110 temperature on the RTD is unifo
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112 in cold water application, it i
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1 ACKNOWLEDGEMENTS The research tha
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3 2.2.3 Todd Harms’ Data ........
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5 LIST OF FIGURES Figure E-1. FDD d
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7 LIST OF TABLES Table E-1 FDD resu
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9 IA = Independence Assumption λ i
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11 EXECUTIVE SUMMARY Packaged air c
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13 current values of the fault indi
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15 Figure E-4 Histogram of the EER
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17 2. Operational cost savings, whi
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19 Table E-5 Conservative Lifetime
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21 constraints. Economic constraint
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23 Paper statistics in HVAC FDD 35
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25 on directional changes to identi
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27 fault levels at different operat
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29 was concluded that the method wa
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31 However, several improvements ar
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33 point sensor placement is genera
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35 2 DATA SOURCES USED FOR EVALUATI
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37 The fives types of faults are re
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39 Occupation Type Climate Location
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41 The SRB FDD method determines wh
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43 The following sections describe
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45 points rather than on a distribu
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47 current operation point P 1, P F
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49 4 A DECOUPLING-BASED FDD TECHNIQ
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51 This approach overcomes the draw
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53 ⎡ ∆T ⎢ ⎢ ∆T 2 ⎢ ∆
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55 improving the compressor model p
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57 Condenser Air Mass Flow Rate (lb
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59 Evaporator Air Mass Flow Rate (l
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61 Liquid-Line Pressure Drop (PSI)
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63 4.2.2 Purdue Field Emulation Sit
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65 1. Evacuated the system, and the
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67 normal _ value − current _ val
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69 Figure 4-16 shows one frame afte
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71 Figure 4-18 shows one frame afte
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73 valve position can be seen from
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Figure 4-22 Outputs of the FDD demo
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77 Figure 4-23 Histogram bar plot o
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83 4.2.3.2 Summarized Results for O
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85 5 ECONOMIC ASSESSMENTS Since the
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87 inspection savings would be $2,0
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89 Q & Cap = W & × EER (5-2) The e
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91 4. electricity costs ( C e ). Ut
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93 Table 5-3 lists estimates of equ
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95 5.4 Smart Service Schedule Savin
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97 7. A 6-ton RTU having a cost of
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99 Location North California South
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101 5. Three case studies were inve
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103 REFERENCES Aaron, D. A., and P.
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105 Davis, Coby. 1993. Comparison o
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107 Rossi, T.M., 1995. Detection, D
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