Fault Detection and Diagnostics for Rooftop Air Conditioners

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6 T evap T cond 5 T hg T sc T sh 4 dT ca dT ea RMS error (F) 3 2 1 0 1 2 3 4 5 6 Training data set NO. Figure 5.2 RMS error with different training data set Training data set number Table 5.1 Data organization for adaptive modeling Driving conditions range T ra (F) T ai (F) T wb (F) 1 73-77.5 70-85 58-62.5 16 2 73-79 70-90 58-64 35 3 71.5-79 65-90 56.5-64 48 4 71.5-80.5 65-95 56.5-65.5 67 5 70-80.5 60-95 55-65.5 96 6 70-82 60-100 55-67 135 Training data amount 57
6. Conclusions and future work So far, • Some literature about FDD modeling has been reviewed • Four black-box modeling approaches: polynomial, BP neural network, RBF and GRNN, were compared using laboratory data • A polynomial plus GRNN modeling approach was proposed in order to improve modeling perfomance and was tested using Purdue field site and one california field site data. Some environmental facors have been investigated in field modeling, • In order to further improve the robustness of FDD modeling, an idea of adaptive FDD modeling was proposed and was simulated using laboratory data Future work will be to, • Continue to complete the modeling work for California sites • Develop and implement a prototype FDD System • Develop an improved FDD method for handling multiple faults that occur simultaneously • Implement improved FDD method in the field sites • Perform an economic assessment of the FDD system for California 58
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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
Page 57 and 58: Table 1.1 Comparisons of Three Mode
Page 59 and 60: solution procedure involves the non
Page 61 and 62: independent of the moisture content
Page 63 and 64: function, f ( X , y) , if it can be
Page 65 and 66: 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: Since air conditioners always cycle
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 and 118: $ !! !! )
Page 119 and 120: Paper statistics in HVAC FDD Number
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
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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" .. !!
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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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