Fault Detection and Diagnostics for Rooftop Air Conditioners

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# ASHRAE. 1993. ASHRAE Handbook - 1993 Fundamentals, Atlanta: American Society of Heating, Refrigerating, and Air Conditioning Engineers, Inc., Atlanta, GA 30329. Braun, J.E. and Li, Haorong. 2001. Description of FDD modeling approach for normal performance expectation. Progress report submitted to Architectural Energy Corporation. Breuker, M.S., 1997. Evaluation of a Statistical, Rule-based Detection an Diagnosis Method for Vapor Compression Air Conditioners, Master’s Thesis, School of Mechanical Engineering, Purdue University. Breuker, M.S. and Braun, J.E., 1998. Common Faults and Their Impacts for Rooftop Air Conditioners, HVAC&R Research, Vol. 4, No. 3, pp.303-318. Breuker, M.S. and Braun, J.E., 1998. Evaluating the performance of a Fault Detection and Diagnostic System for Vapor Compression Equipment, International Journal of Heating, Ventilating, and Air Conditioning and Refrigerating Research, Vol. 4, No. 4, pp.401-425. Cacoullos, 1966. “Estimation of a multivariate density,” Ann. Inst. Statist. Math, (Tokyo), Vol. 18, no. 2,pp.179-189. Carling, P., 2002. Comparison of Three Fault Detection Methods Based on Field Data of an Air-Handling Unit, ASHRAE Transactions 2002, V. 108, Pt.1. Chen, B., 2000. Evaluating the potential of on-line fault detection and diagnosis for rooftop air conditioners, Master’s Thesis, School of Mechanical Engineering, Purdue University. 59
Chen, Bin and Braun, J.E, 2000. Simple fault detection and diagnostics methods for packaged air conditioners, Proceedings of the 8 th international refrigeration conference, Purdue University. Chen, Bin and Braun, J.E, 2001. Simple rule-based methods for fault detection and diagnostics applied to packaged air conditioners, ASHRAE Trans., vol. 107, part 1, paper No. AT-01-14-2, 847-857. Comstock, M.C., J.E. Braun, and B. Chen, 1999, Literature Review for Application of Fault Detection and Diagnostic Methods to Vapor Compression Cooling Equipment, Purdue University, Ray W. Herrick Laboratories, Report # HL99-19. Davis, Coby. 1995. Comparison of steady state detection algorithms. ME 597 Report. Available from Professor Jim Braun, Herrick Labs, Purdue University, West Lafayette, In. Dexter, Arthur; Pakanen, Jouko (ed.). Demonstrating Automated Fault Detection and Diagnosis Methods in Real Buildings. Espoo. VTT Building Technology, 2001. VTT Symposium 217. 381 p. + app. 13 p. Dexter, A.L. and Ngo, D., 2001. Fault Diagnosis in Air-Conditioning Systems: A Multi- Step Fuzzy Model-Based Approach, International Journal of Heating, Ventilating, and Air Conditioning and Refrigerating Research, Vol. 7, No. 1, pp.83-102. Donald F. Specht, 1991. A General Regression Neural Network. IEEE Transactions on Neural Networks. 2 (6): 568-576. Dyn, N. and Levin, D.,1983. “Iterative solutions of systems originating from integral equations and surface interpolation.” SIAM J. Numerical Analysis, vol. 20, pp. 377-390. Ghiaus, C. 1999. Fault diagnosis of air conditioning systems based on qualitative bond gragh, Energy and Buildings 30 (1999) 221-132. 60
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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
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prototype was developed by using th
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3. Comparison of black-box modeling
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which is more accurate than the exp
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Similar to GRNN, RBF has very good
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Table 3.2 RMS error (Polynomial,GRN
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Table 3.4 Contrast of Black-box mod
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Polynomial plus GRNN Training Desir
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interpolation(poly+GRNN) extrapolat
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used to build the steady-state mode
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α , ρ, τ I t t s h o A t a Figur
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Temperature (F) 82 79 76 73 Condens
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0.050 0.045 Pressure = 101.3 [kPa]
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T T − T − T W = W −W −W m =
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Figure 4.8 Output of three steady-s
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will be calculated to represent the
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180 175 RMS error = 0.3984 (F) Pred
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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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$ !! !! )
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
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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
Page 169: $ /) $ 0111" .. !!
Page 173 and 174: Ventilating, Air-Conditioning and R
Page 175 and 176: 1.T amb 2.T ra 3.RH ra Plant Prepro
Page 177 and 178: F) $
Page 179 and 180: +: + $ 5
Page 181 and 182: .44(' * 5 09( ( 0( F N ( M , Σ)
Page 183 and 184: Load 20% 40% 60% 80% 100% 3 4 Fault
Page 185 and 186: Load 20% 40% 60% 80% 100% 3 4 Fault
Page 187 and 188: 3 4 0 0 0 0 0.4173 0 0.0010 0 0.000
Page 189 and 190: .44(' $ () ) ) - $
Page 191 and 192: ∆P Restrictio n Level=100%* ∆P
Page 193 and 194: 2 TABLE OF CONTENTS TABLE OF CONTEN
Page 195 and 196: 4 A1.2.3 Valve Position Expression
Page 197 and 198: 6 Figure 2-10 Decoupling refrigeran
Page 199 and 200: 8 LIST OF TABLES Table 1-1 Fault di
Page 201 and 202: 10 N = Number of generated sample p
Page 203 and 204: 12 with a fixed-orifice as the expa
Page 205 and 206: 14 (residuals) should have a zero m
Page 207 and 208: 16 expected distribution of the res
Page 209 and 210: 18 operation. Another advantage is
Page 211 and 212: 20 1.1.2.1 Original SRB Fault Diagn
Page 213 and 214: 22 Corresponding to the SRB fault d
Page 215 and 216: 24 integration of the probability d
Page 217 and 218: 26 1. Simplifies fault detection fr
Page 219 and 220: 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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