Fault Detection and Diagnostics for Rooftop Air Conditioners

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2002, or remotely if possible. Change back to the same settings for each room as with the first cooling season phase of May-June, 2001. RESTAURANTS: March – May 2001: Monitor building performance for each of the restaurants using one common ventilation control strategy. This will likely be the use of the existing economizer control. Use this time to build baseline data for each building. During this time, visit each site (March and/or May) and characterize the nominal usage patterns, etc. June-July, 2001: For each climate type, set up one building with DCV mode and the other with normal economizer mode. (Sacramento sites have Honeywell economizers currently installed.) August-Fall, 2001: At each climate type, reverse the ventilation control strategies, with one building using DCV and the other set-up for fixed position dampers. November 2001 – December/January 2002: Maintain the same control strategy for each building for the beginning of the heating season. December 2001 – February 2002: Reverse ventilation control strategies between the buildings at each climate type. (Do this during a site visit in December 2001 or January 2002.) Change back to the same settings for each room as with the first cooling season phase of June-July, 2001. RETAIL STORES The detailed plan for monitoring the retail stores will be finalized after completion of the equipment installation. The plan will likely be as follows. August-Fall, 2001: After the initial installation and checkout of the control equipment, begin to monitor the buildings at the inland and coastal climate sites with one building in DCV mode and the other using normal economizer control mode. 41
November 2001 – December/January 2002: Maintain the same control strategy for each building for the beginning of the heating season. December 2001 – February 2002: Reverse ventilation control strategies between the buildings at each climate type. (Do this during a site visit in December 2001 or January 2002.) Spring 2002: Reverse the ventilation control strategies from the cooling season data gathered during August and the fall of 2001. 42
Page 1 and 2: CALIFORNIA ENERGY COMMISSION Final
Page 3 and 4: Acknowledgements Jim Braun and Haor
Page 5 and 6: Abstract Project 2.1, Fault Detecti
Page 7 and 8: TABLE OF CONTENTS LIST OF TABLES LI
Page 9 and 10: LIST OF FIGURES Page 1 - Field Test
Page 11 and 12: The report “Description of Labora
Page 13 and 14: mounted heat pumps for heating and
Page 15 and 16: The retail stores are in Southern C
Page 17 and 18: Figure 1 - Field Test Sites Data Co
Page 19 and 20: Gibson School (Cont’d) Woodland S
Page 21 and 22: Table 1 - Data List for Modular Sch
Page 23 and 24: BUILDING TYPE: Modular School Rooms
Page 25 and 26: HEATING / AIR CONDITIONING EQUIPMEN
Page 27 and 28: Sacramento Area McDonalds PlayPlace
Page 29 and 30: Bradshaw Road (Sacramento Area) McD
Page 31 and 32: TEST INSTRUMENTATION: Tables 2 and
Page 33 and 34: Table 2 - Data List for Inland Rest
Page 39 and 40: Castro Valley (San Francisco Bay Ar
Page 41 and 42: Castro Valley McDonalds PlayPlace P
Page 43 and 44: more or less custom design, publish
Page 45 and 46: BUILDING TYPE: Retail Store ADDRESS
Page 47 and 48: TEST INSTRUMENTATION: Similar test
Page 49: • Temperature and humidity levels
Page 53 and 54: Table of Contents 1. Introduction..
Page 55 and 56: 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
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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" .. !!
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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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