Fault Detection and Diagnostics for Rooftop Air Conditioners

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85 REFERENCE Aaron, D. A., and P. A. Domanski. 1990. Experimentation, analysis, and correlation of refrigerant-22 flow through short-tube restrictor. ASHRAE Transactions 96(1): 729-742. Alistair I. Mees, M. F. Jackson, and Leon O. Chua, 1992. Device Modeling by Radial Basis Functions. IEEE Transactions on Circusits and Systems-Fundmental Theory and Applications. 39 (1): 19-27. ASHRAE. 1993. ASHRAE Handbook - 1993 Fundamentals, Atlanta: American Society of Heating, Refrigerating, and Air Conditioning Engineers, Inc., Atlanta, GA 30329. Benjamin, M.W., and J.G. Miller,1941. The flow of saturated water throttling orifice. Transactions of the ASME 63 (5): 419-426 Braun, J.E. and Li, Haorong. 2001a. Description of Laboratory Setup. Progress report submitted to Architectural Energy Corporation. Braun, J.E. and Li, Haorong. 2001b. Description of FDD modeling approach for normal performance expectation. Progress report submitted to Architectural Energy Corporation. Braun, J.E. and Li, Haorong. 2002. Description and Evaluation of an Improved FDD Method for Rooftop Air Conditioners. 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. 85
86 Breuker, M.S. and Braun, J. E., 1997. Evaluation of a Statistical, Rule-based Detection an Diagnosis Method for Vapor Compression Air Conditioners, HL 98-9, Report # 1796-7a, 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.401-423. 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. 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, 847-837. 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. 1993. Comparison of steady state detection algorithms. ME 397 Report. Available from Professor Jim Braun, Herrick Labs, Purdue University, West Lafayette, In. 86
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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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$ !! !! )
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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
Page 225 and 226: 34 1.2.3 Decoupling of Component Fa
Page 227 and 228: 36 1.2.3.2 Condenser-Related Faults
Page 229 and 230: 38 mixture, χ ref is known as the
Page 231 and 232: 40 Table 1-3 also lists the refrige
Page 233 and 234: 42 Refrigerant Property T cond, pre
Page 235 and 236: 44 as an independent feature only f
Page 237 and 238: 46 evaporator air flow rate reducti
Page 239 and 240: 48 5. 2 Pll ∆ deviates drasticall
Page 241 and 242: 50 System-Level Faults RefUnder Ref
Page 243 and 244: 52 1.2.5 Summary of Decoupling Sche
Page 245 and 246: 54 noise, system disturbances and m
Page 247 and 248: 56 compressor data. When there is a
Page 249 and 250: 58 Figure 2-5 Decoupling evaporator
Page 251 and 252: 60 Total Pressure Drop of Liquid- L
Page 253 and 254: 62 Figure 2-11 Illustration of Demo
Page 255 and 256: 64 bypass the compressor. At this t
Page 257 and 258: 66 for T dis because there is no ov
Page 259 and 260: 68 Figure 2-14 Outputs of the FDD d
Page 261 and 262: 70 the system requires more refrige
Page 263 and 264: 72 recommended that: the system req
Page 265 and 266: 74 Figure 2-20 Outputs of the FDD d
Page 267 and 268: 76 Figure 2-21 Histogram bar plot o
Page 273 and 274: 82 2.3.2 Summarized Results for Oth
Page 275: 84 3 CONCLUSIONS AND RECOMMENDATION
Page 279 and 280: 88 Lee, W., House, J.M. and Shin, D
Page 281 and 282: 90 APPENDIX 1 PHYSICAL MODELS OF EX
Page 283 and 284: 92 A1.1.2 Short-Tube Models Many re
Page 285 and 286: 94 m& = CA ρ P up − P ) (A1-5) (
Page 287 and 288: 96 The abrupt change of CA for the
Page 289 and 290: 98 These equations can be combined
Page 291 and 292: 100 H h T sh , max opening T sh , s
Page 293 and 294: 102 A1.2.5 Parameter Estimation Met
Page 295 and 296: 104 T (2 T sh, ratingopening , sh,m
Page 297 and 298: 106 Harms’Result Harms plotted al
Page 299 and 300: 108 Mass flow rate Local linearizat
Page 301 and 302: 110 temperature on the RTD is unifo
Page 303 and 304: 112 in cold water application, it i
Page 305 and 306: 1 ACKNOWLEDGEMENTS The research tha
Page 307 and 308: 3 2.2.3 Todd Harms’ Data ........
Page 309 and 310: 5 LIST OF FIGURES Figure E-1. FDD d
Page 311 and 312: 7 LIST OF TABLES Table E-1 FDD resu
Page 313 and 314: 9 IA = Independence Assumption λ i
Page 315 and 316: 11 EXECUTIVE SUMMARY Packaged air c
Page 317 and 318: 13 current values of the fault indi
Page 319 and 320: 15 Figure E-4 Histogram of the EER
Page 321 and 322: 17 2. Operational cost savings, whi
Page 323 and 324: 19 Table E-5 Conservative Lifetime
Page 325 and 326: 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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77 Figure 4-23 Histogram bar plot o
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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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