Fault Detection and Diagnostics for Rooftop Air Conditioners

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10 σ = Standard deviation σ normal = σ for normal operation Σ = Covariance matrix Σ = Σ with independence assumption assumption Σ current = Σ for current operation Σ = Σ for normal operation normal Σ = True covariance matrix true Σ X = Covariance matrix for X T = Runtime T = Ambient temperature amb T = Condensing temperature cond T dis = Discharge line temperature TELS = Total equipment life savings T = Evaporating temperature T ll evap = Liquid line temperature T = Mixed air temperature T T T T ma oa ra sc sh T wb UCS USS VAV v 1 X Y i i = Outdoor air temperature = Return air temperature = Sub-cooling = Superheat = Wet bulb temperature = Utility cost savings = Unnecessary service savings = Variable air volume = Specific volume of compressor inlet refrigerant = Sample point in original space = Sample point in transformed space W & = Power consumption W & = Average power consumption W ma = Mixed air humidity ratio W oa = Outdoor air humidity ratio = Return air humidity ratio W ra
11 EXECUTIVE SUMMARY Packaged air conditioning equipment is used extensively throughout small commercial and institutional buildings. However, compared to larger systems, they tend to be not well maintained. Widespread application of automated fault detection and diagnosis (FDD) to packaged equipment will significantly reduce energy use and peak electrical demand, down time and maintenance costs. However, techniques for online FDD reported in the literature are expensive to apply because of requirements for model training and large computation, have only been tested in the laboratory, and can not handle multiple-simultaneous faults. Furthermore, no one has performed economic assessments of FDD. Economic assessment is a complicated problem that requires field evaluations. The primary goals of the research described in this report were to 1) develop a practical automated FDD technique having low cost, robust performance, and the capability to handle multiple-simultaneous faults and 2) to perform an initial economic assessment of FDD applied to vapor compression equipment in California. The process of developing and evaluating the performance of FDD methods involved the use of both laboratory and field data. Laboratory data from previous studies were used to provide rigorous performance evaluations. In addition, a number of field sites were established in small commercial buildings in California to allow consideration of practical issues. An additional field site was set up at Purdue to allow artificial implementation of faults in order to provide more controlled evaluation of the FDD techniques under realistic operating conditions. Two different FDD approaches were developed in this research. First of all, the statistical rule-based (SRB) FDD method presented by Rossi and Braun (1997) was modified to improve sensitivity and robustness and reduce computational requirements. The following components of the FDD method were improved: 1) models for predicting
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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
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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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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 and 276: 84 3 CONCLUSIONS AND RECOMMENDATION
Page 277 and 278: 86 Breuker, M.S. and Braun, J. E.,
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: 9 IA = Independence Assumption λ i
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
Page 327 and 328: 23 Paper statistics in HVAC FDD 35
Page 329 and 330: 25 on directional changes to identi
Page 331 and 332: 27 fault levels at different operat
Page 333 and 334: 29 was concluded that the method wa
Page 335 and 336: 31 However, several improvements ar
Page 337 and 338: 33 point sensor placement is genera
Page 339 and 340: 35 2 DATA SOURCES USED FOR EVALUATI
Page 341 and 342: 37 The fives types of faults are re
Page 343 and 344: 39 Occupation Type Climate Location
Page 345 and 346: 41 The SRB FDD method determines wh
Page 347 and 348: 43 The following sections describe
Page 349 and 350: 45 points rather than on a distribu
Page 351 and 352: 47 current operation point P 1, P F
Page 353 and 354: 49 4 A DECOUPLING-BASED FDD TECHNIQ
Page 355 and 356: 51 This approach overcomes the draw
Page 357 and 358: 53 ⎡ ∆T ⎢ ⎢ ∆T 2 ⎢ ∆
Page 359 and 360: 55 improving the compressor model p
Page 361 and 362: 57 Condenser Air Mass Flow Rate (lb
Page 363 and 364: 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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