Fault Detection and Diagnostics for Rooftop Air Conditioners

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* ) +, -./ 0 122 * 3++, - 01234 5 ( !! !! 6.06.6 !! 07 0118 " %" + $ / 0111" 0111 0111 .. Paper statistics in HVAC FDD 35 30 25 Number 20 15 10 5 0 1987-1989 1990-1992 1993-1995 1996-1998 1999-2001 Time Figure 2-1 Paper statistics in HVAC FDD with time 7
Paper statistics in HVAC FDD Number 40 35 30 25 20 15 10 5 0 AHU <strong>and</strong> VAV Packaged <strong>Air</strong> Conditioner Chiller Controller Other System Figure 2-2 Paper statistics in HVAC FDD with system ** 4 + 011193 -'::';9< ! =$ 6330" • ) . !! • ) $ • ) . !! • & • !! ** 5( ( 0 / 0117 011>" !! - 012
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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
Page 67 and 68: prototype was developed by using th
Page 69 and 70: 3. Comparison of black-box modeling
Page 71 and 72: which is more accurate than the exp
Page 73 and 74: Similar to GRNN, RBF has very good
Page 75 and 76: Table 3.2 RMS error (Polynomial,GRN
Page 77 and 78: Table 3.4 Contrast of Black-box mod
Page 79 and 80: Polynomial plus GRNN Training Desir
Page 81 and 82: interpolation(poly+GRNN) extrapolat
Page 83 and 84: used to build the steady-state mode
Page 85 and 86: α , ρ, τ I t t s h o A t a Figur
Page 87 and 88: Temperature (F) 82 79 76 73 Condens
Page 89 and 90: 0.050 0.045 Pressure = 101.3 [kPa]
Page 91 and 92: T T − T − T W = W −W −W m =
Page 93 and 94: Figure 4.8 Output of three steady-s
Page 95 and 96: will be calculated to represent the
Page 97 and 98: 180 175 RMS error = 0.3984 (F) Pred
Page 99 and 100: 180 175 RMS error =1.1472 (F) Predi
Page 101 and 102: 51 50 RMS error=0.3860 (F) Predicte
Page 103 and 104: 4.4 California field site results S
Page 105 and 106: 105 100 RMS error =0.5982 (F) Predi
Page 107 and 108: Since air conditioners always cycle
Page 109 and 110: 6. Conclusions and future work So f
Page 111 and 112: Lee, W., House, J.M. and Shin, D.R.
Page 113 and 114: Table of Contents 1 Introduction...
Page 115 and 116: AHU α β 2 d i EER ∆ ∆ η v T
Page 117: $ !! !! )
Page 121 and 122: 011>" - , ?" 8?"
Page 123 and 124: + : 6336" ,
Page 125 and 126: + :: !!- :: !!
Page 127 and 128: 6 24 122 7) 6 3++, ) . !! /011
Page 129 and 130: )
Page 131 and 132: 336 F / s $ S
Page 133 and 134: N $ V v1
Page 135 and 136: Figure 3-4 2-dimensional residual d
Page 137 and 138: 10 5 Normal and current operation p
Page 139 and 140: 7 N Ω f N N Ω = Ω X ,
Page 141 and 142: 0 + α 6 d χ ( )
Page 143 and 144: Normal operation region Residual-2
Page 145 and 146: Table 3-2 Refrigerant Leak at 20% l
Page 147 and 148: ratio dist = P F 1 P F 1 2 1 9.0
Page 149 and 150: $ , " - (
Page 151 and 152: )(( 0( 04 ) 6330" /
Page 153: Table 4-2 Polynomial plus GRNN mode
Page 157 and 158: 4.2 m r,predict (kg/min) 4 3.8 3.6
Page 159 and 160: 6 1)( ( ( 6 1)( ) , ( )
Page 161 and 162: - )
Page 163 and 164: ? "J> *"J060 #"J083 *$"J670 #"J08<
Page 165 and 166: Table 4-17 Detected (normal, fault)
Page 167 and 168: c=1 c=10 c=20 1 0.8 Distance ratio
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$ /) $ 0111" .. !!
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Chen, Bin and Braun, J.E, 2000. Sim
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Ventilating, Air-Conditioning and R
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1.T amb 2.T ra 3.RH ra Plant Prepro
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F) $
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+: + $ 5
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.44(' * 5 09( ( 0( F N ( M , Σ)
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Load 20% 40% 60% 80% 100% 3 4 Fault
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Load 20% 40% 60% 80% 100% 3 4 Fault
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3 4 0 0 0 0 0.4173 0 0.0010 0 0.000
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.44(' $ () ) ) - $
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∆P Restrictio n Level=100%* ∆P
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2 TABLE OF CONTENTS TABLE OF CONTEN
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4 A1.2.3 Valve Position Expression
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6 Figure 2-10 Decoupling refrigeran
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8 LIST OF TABLES Table 1-1 Fault di
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10 N = Number of generated sample p
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12 with a fixed-orifice as the expa
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14 (residuals) should have a zero m
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16 expected distribution of the res
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18 operation. Another advantage is
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20 1.1.2.1 Original SRB Fault Diagn
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22 Corresponding to the SRB fault d
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24 integration of the probability d
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26 1. Simplifies fault detection fr
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28 Step i Do FDD on fault i . After
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30 ROOFTOP UNIT FAULTS COMPONENT-LE
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32 has two possible causes: refrige
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34 1.2.3 Decoupling of Component Fa
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36 1.2.3.2 Condenser-Related Faults
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38 mixture, χ ref is known as the
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40 Table 1-3 also lists the refrige
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42 Refrigerant Property T cond, pre
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44 as an independent feature only f
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46 evaporator air flow rate reducti
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48 5. 2 Pll ∆ deviates drasticall
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50 System-Level Faults RefUnder Ref
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52 1.2.5 Summary of Decoupling Sche
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54 noise, system disturbances and m
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56 compressor data. When there is a
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58 Figure 2-5 Decoupling evaporator
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60 Total Pressure Drop of Liquid- L
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62 Figure 2-11 Illustration of Demo
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64 bypass the compressor. At this t
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66 for T dis because there is no ov
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68 Figure 2-14 Outputs of the FDD d
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70 the system requires more refrige
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72 recommended that: the system req
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74 Figure 2-20 Outputs of the FDD d
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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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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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Fault Detection and Diagnostics for Rooftop Air Conditioners

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