Fault Detection and Diagnostics for Rooftop Air Conditioners

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Preface The Public Interest Energy Research (PIER) Program supports public interest energy research and development that will help improve the quality of life in California by bringing environmentally safe, affordable, and reliable energy services and products to the marketplace. The Program’s final report and its attachments are intended to provide a complete record of the objectives, methods, findings and accomplishments of the Energy Efficient and Affordable Commercial and Residential Buildings Program. This attachment is a compilation of reports from Project 2.1, Fault Detection and Diagnostics for Rooftop Air Conditioners, providing supplemental information to the final report (Commission publication #P500-03-096). The reports, and particularly the attachments, are highly applicable to architects, designers, contractors, building owners and operators, manufacturers, researchers, and the energy efficiency community. This document is one of 17 technical attachments to the final report, consolidating five research reports from Project 2.1: • Description of Field Test Sites (Feb 2003, rev.) • Description of FDD Modeling Approach For Normal Performance Expectation (Dec 2001) • Description And Evaluation Of An Improved FDD Method For Rooftop Air Conditioners (Aug 2002) • Decoupling-Based FDD Approach For Multiple Simultaneous Faults (June 2003) • Automated Fault Detection and Diagnostics of Rooftop Air Conditioners For California, Final Report and Economic Assessment (Aug 2003) The Buildings Program Area within the Public Interest Energy Research (PIER) Program produced this document as part of a multi-project programmatic contract (#400-99-011). The Buildings Program includes new and existing buildings in both the residential and the nonresidential sectors. The program seeks to decrease building energy use through research that will develop or improve energy-efficient technologies, strategies, tools, and building performance evaluation methods. For the final report, other attachments or reports produced within this contract, or to obtain more information on the PIER Program, please visit www.energy.ca.gov/pier/buildings or contact the Commission’s Publications Unit at 916-654-5200. The reports and attachments, as well as the individual research reports, are is also available at www.archenergy.com. iv
Abstract Project 2.1, Fault Detection and Diagnostics for Rooftop Air Conditioners. Packaged air conditioners are the most poorly maintained type of HVAC system. In California, they use about 54% of the HVAC energy in the commercial sector. The Purdue research team developed thermo-fluids based fault detection methods that can pinpoint five common maintenance problems. • This project was highly successful, resulting in a cost-effective method to detect simultaneous faults using only temperature sensors and models of normal operations. • Controllers that embed these diagnostics methods will save energy and maintenance costs by providing alerts only when maintenance is needed and giving the mechanic better information. • The historical data from the diagnostic system will also serve as a database for manufacturers to improve the reliability of components. This document is a compilation of five technical reports from the research. v
Page 1 and 2: CALIFORNIA ENERGY COMMISSION Final
Page 3: Acknowledgements Jim Braun and Haor
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 and 52: November 2001 - December/January 20
Page 53 and 54: 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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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
Page 411:
107 Rossi, T.M., 1995. Detection, D
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Fault Detection and Diagnostics for Rooftop Air Conditioners

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