Fault Detection and Diagnostics for Rooftop Air Conditioners

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DESCRIPTION OF FDD MODELING APPROACH FOR NORMAL PERFORMANCE EXPECTATION Deliverable 2.1.2 Progress report submitted to: Architectural Energy Corporation For the Building Energy Efficiency Program Sponsored by: California Energy Commission Submitted By: Purdue University Principal Investigator: Research Assistant: James Braun, Ph.D., P.E. Haorong Li December 2001 Mechanical Engineering 1077 Ray W. Herrick Laboratories West Lafayette, IN 47907-1077 (765) 496-6008 (765) 494-0787 (fax) i
Table of Contents 1. Introduction....................................................................................................................4 2. Background work...........................................................................................................7 2.1 Physical modeling and gray-box modeling.......................................................................7 2.2 Black-box modeling.............................................................................................................8 2.2.1 Polynomials...................................................................................................................................8 2.2.2 General Regression Neural Network..........................................................................................11 2.2.3 Back-propagation neural network ..............................................................................................15 2.2.4 Radial basis function...................................................................................................................16 3. Comparison of black-box modeling approaches using laboratory data ..................18 3.1 Laboratory experimental data.........................................................................................18 3.2 Comparisons.......................................................................................................................21 3.3 Polynomial plus GRNN model.........................................................................................27 4. Modeling field site data................................................................................................31 4.1 Impact of unmeasured weather variables ......................................................................31 4.1.1 Solar radiation impact on the system .........................................................................................31 4.1.2 Rainfall impact on the system ....................................................................................................35 4.2 Data Processing..................................................................................................................37 4.2.1 Driving condition problems........................................................................................................37 4.2.2 Steady-State Detector .................................................................................................................40 4.2.3 Clustering of steady-state data....................................................................................................43 4.3 Purdue Field Site Results..................................................................................................44 4.4 California Rooftop unit model .........................................................................................52 5. Adaptive Polynomial plus GRNN ...............................................................................55 6. Conclusions and future work......................................................................................58 7. Reference ......................................................................................................................59 2
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 and 50: • Temperature and humidity levels
Page 51: November 2001 - December/January 20
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
Page 101 and 102: 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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