Fault Detection and Diagnostics for Rooftop Air Conditioners

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0.025 Indoor Conditions for Testing Training Data Test Data 0.80 0.020 0.60 0.015 64 F 0.40 0.010 55 F 58 F 61 F 0.20 0.005 0.000 60 70 80 90 T [F] Figure 3.1 Indoor conditions simulated for training and testing steady-state models The training data were gathered by running the rooftop unit with no faults. Data was gathered over the course of three weeks of testing. To ensure that there were no faults developing in the test unit over this time period and to test the experimental noise which is present in the operation of the test unit, a test condition at T amb = 85 F, T ra = 76 F, Φ ra = 42% was retested every few days. The results of this repeatability test for all of the measurements used by the FDD technique are shown in table 3.1. The most noisy measurements, as expected, are the suction superheat and hot gas temperatures. This level of experimental noise is already considerably above the measurement error levels which were used in the steady-state analysis of the FDD technique performed by Rossi. Figure 2.2 shows the level of suction superheat during the repeatability test. The fact that the graph is not increasing during the test indicates that the level of charge in the system is not decreasing, since suction superheat is particularly dependent on the charge level in the system. One cannot expect to develop a model 19
which is more accurate than the experimental noise of the measurements which are used in learning the model. Table 3.1 Repeatability analysis during steady-state model testing Property T evap T sh T hg T cond T sc ∆T ca ∆T ea of Data (deg. F) Mean 43.76 8.42 195.11 108.69 7.05 11.72 19.14 Std. Dev. 0.39 1.73 1.25 0.40 0.25 0.09 0.17 Spread 1.21 4.80 4.04 1.28 0.60 0.26 0.64 Suction Superheat vs. Time During Repeatability Test 11 10 Suction Superheat (F) 9 8 7 6 5 5/3/97 5/7/97 5/9/97 5/11/97 5/17/97 5/18/97 5/19/97 5/20/97 5/21/97 5/22/97 Date Figure 3.2 Suction superheat during repeatability test 20
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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
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 and 50: • Temperature and humidity levels
Page 51 and 52: November 2001 - December/January 20
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: 3. Comparison of black-box modeling
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 and 118: $ !! !! )
Page 119 and 120: 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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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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