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Embedded Controllers for Increasing HVAC Energy EfficiencyIncreasing HVAC Energy Efficiency
by Automated Fault Diagnostics
J. Vass, J. Trojanová, R. Fišera, J. Rojíček, j , , j
Honeywell Prague LaboratoryAutomation & Control Solutions
GREEMBED Workshop, Stockholm, April 12th 2010
Outline
• Honeywell Building Solutions (HBS)
• JACE controller (embedded system)
• HVAC diagnostics- Air Handling Unit (AHU)
• AFDD algorithmD t Cl i- Data Cleansing
- Mode Detection- System Observationy- Fault Isolation
• Examples
Honeywell
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Specialty 12% Aerospace
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More than 50,000 employeesProfileProfile
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Embedded System (JACE)
• JACE = JAVA® Application Control Engine• Integration Controller
Integrator- Integrator Integration of communication protocols (LON, BACnet, Modbus etc.)Graphical interface & Built-in web serverData processing capability (e.g. AFDD)
- Controller Freely programmable DDC controllerFreely programmable DDC controller
Multiple protocols
Graphical interface
JACE
interface(e.g. AHU schematics)
JACE: Integration ControllerI t ti f i b ildi b t• Integration of various building subsystems- HVAC; Lighting; Metering
• Multiple protocols to communicate with:p p- Plant controllers (Boiler, Chiller, Air handlers)- Room controllers (VAV terminals, Fan coil units)
JACE
I/O modules Sensors
ActuatorsWWW interface
AHU1 AHU2 AHU3
JACE
Energymeters
Bli dLi htiBoiler Chiller RTU
VAV1 VAV2XL10 XL12
BlindsLighting
FCU1XL10
Next Generation of JACE controllers
• HVAC Diagnostics in JACE- Automated fault detectionHVAC equipment - Boilers, Chillers, AHUs, VAV terminal units, Fan coils Auxiliary devices - Pumps, Fans, Valves, etc.
- Present AFDD results using:Present AFDD results using:Online diagnostic screens - GUIDiagnostic reports - Charts, tables, recommendations
• Challenge = Address practical aspects:Diff t l l f i t t ti- Different levels of instrumentation (Scenarios according to available sensors)
- Contextual information from BIM( A t ti ll dif di ti l b d i HVAC(e.g. Automatically modify diagnostic rules based on given HVAC configuration)
- Monetization(C d d d i f fi i l l )(Convert degraded equipment performance to financial loss)
HVAC Equipment
exhaust air
Air Handlers Fans
VAV Boxes
H Cfresh
coils
air
Pumps
compression tank Chillers
boilercombustion gas
evaporator
filter
Boilers
gas
drain
condenser
compressorcooling towercondenser
water pumpHW circulating
pumpfuelair
p p
Air Handling Unit (AHU)
from exhaust
Return air temperatureReturn air relative humidity
zones
Occupancy
to
p ystatusDamper position
to zonesoutdoor
air
H C
Supply air temperatureO td i Mixed air Supply air Supply air temperature- set point
Outdoor air temperature
Mixed air temperature
Supply air temperature
Outdoor air relative humidity
hot water chilled waterHeating/cooling coil valve control signal
y
Types of AHU Faults
• Hardware Faults- Abrupt Faults
• Control System Faults- Control Loop Performance
Damper faults - stuck damper Valve faults - stuck or leaking
valve (of heating/cooling coil)
Missed setpoint (permanent offset)Oscillating control signal - e.g. due
to wrong PID parametersHeating/cooling failure Sensor faults - frozen sensor,
noisy data, missing values,
Excessive overshoot Long settling time
- Control Strategyoutliers- Performance Degradation Air filter clogging
Control Strategy Simultaneous heating & coolingUsing mechanical cooling instead
of ventilation (free cooling)gg gHeating/cooling coil scaling Sensor drift/bias
of ventilation (free cooling)Wrong sequencing (Late/early
start & stop)
AFDD implementation in JACE
Module 1: Data Cleansing
Control Signals: Oscillations & Outliers
no oscillations (valid control strategy)
oscillations (control strategy fault)
outliers
zoomed view
outliers
oo ed e• oscillatory control signal causes:
- unnecessary opening & closing of the valves- unnecessary movement of the dampersy p
• this results in premature wear and failure of:- valves- actuators
Method 1: Oscillation Detector
higher standard deviation lower standard deviation
raw data
1 STD [ ]STD Ratio =
1 STD [ ]x ky k
smoothed data(low-pass filtered)
threshold = 3
Method 2: Outlier Detector
low-pass filtered
high pass filtered
[ ] [ ] [ ]z k x k y k
high-pass filtered
three-sigma limits:
[ ] 3 [ ]z zUCL k k [ ] 3 [ ]z zLCL k k
three sigma limits:
Module 2: Mode Detection
AHU Operation Mode
100%Mi i b
Control signal
Heating Cooling il l
Mixing box damper
coil valve coil valve
0%
min
Outdoor air temperature
Mode 1
Heating VentilationMode 2 Mode 3
Cooling gmode mode mode
Mode 4
Unknownmode
AHU Operation Mode
• AHU mode- Determined by controller using temperatures (or enthalpies)
AFDD algorithm detects AHU mode using control signals only- AFDD algorithm detects AHU mode using control signals only• Finding stabilized mode:
- Heating mode: ch > 30 minHeating mode: ch > 30 min- Cooling mode: cc > 30 min- Ventilation mode: cv > 30 min
Sampling period (e.g. 5 min)
Threshold on control signals
( 1) if ( ) 2% and ( ) 2%( ) s i iCCh HCtc UT t tU
Counter for
( g ) g
( ) ( ) ( )( )
0 otherwise
( 1) if ( ) 2% and ( ) 2%
s i i
s i i
CC
c CC
h HCh
HC
c
U
t
t T t tc U
Counter for Heating mode
Counter for ( ) ( ) % ( ) %( )
0 otherwise
( 1) if ( ) 2% and ( ) 2%
s i ic CCc
C
H
C
C
i iCHc
t
t T t tU
c
U
Counter for Cooling mode
Counter for ( 1) if ( ) 2% and ( ) 2%( )
0 otherwises C
vCi iCv Hc
ct T t t
tU U
Counter for Ventilation mode
Module 3: System Observation
Vector of Measurements: CCHCSASSAMA UUTTT ,,,,
Vector of Measurements:
Observation o2 Function of supply air temp & setpoint
Th ibl l 1 0 1
if1
Three possible values: 1, 0, -1
T
T
2 if 0if 1
)(
SASSA
SASSA
TTTT
o setpoint is met
above setpoint
T
T2
if 1)(
SASSA
SASSA
TTp
below setpoint
Observed State
• Mapping between observations & states- Each state defined by particular combination of o1, o2 & o3
• 45 possible states of the AHU (states are mutually exclusive)• 45 possible states of the AHU (states are mutually exclusive)- States are classified as normal and abnormal
Heating Mode Cooling Mode Ventilation ModeHeating Mode Cooling Mode Ventilation Mode
Module 4: Fault Isolation & Identification
Mapping Table
• Mapping between abnormal states & faults- Binary diagnostic matrix- Represents the expert knowledge
1 f lt1 = fault0 = don't know
State s12 implies faultsF2 F3 & F7F2, F3 & F7
State s1implies faultsF2 & F6
Fault Aggregation
• Fault Relevance is updated in time using CUSUM: )1(),(,0max)( tRksfmtR ijii )(),(,)( f ijii
Previousvalue
Currentvalue
Forgetting factor k=0 3
Mapping function output 0 or 1 valuevalue factor, k=0.3output, 0 or 1
vanc
et R
elev
Faul
t
Example 1: Stuck Heating Valve
• Air was heated despite UHC = 0 (heating control signal)• Controller compensates by increasing UCC (cooling control signal)• Simultaneous heating & cooling wasted energy financial losses• Simultaneous heating & cooling wasted energy financial losses
detected by d t l i
coolingdata cleansing
e
ventilation
Most likelyl
evan
ce
Most likely fault is F2(stuck heating
only one AHU state
at each time
ault
Rel heating
valve)instant
(confirmedFa
correct fault cannot be isolated
(confirmed by building technician)
Example 2: Oscilating control signalC With D t Cl iWithout Data Cleansing With Data Cleansing
Data cleansing needed to avoid detection of non-existing faults
Summary
• AFDD algorithm for HVAC equipment- HVAC system = Major energy consumer in buildings
Red ce energ asting b a tomated detection of HVAC fa lts- Reduce energy wasting by automated detection of HVAC faults Abrupt hardware faults; Performance degradation
- Performed by Honeywell JACE controller (embedded system)- Graphical visualization in JACE AHU scheme, Measured data, Observed state, Fault relevances
AHU diagnostics• AHU diagnostics- Rule-based diagnostics (based on APAR by Schein et al.)- Data cleansing moduleData cleansing moduleDetect raw data errors (outliers, oscillations, etc.) Protects the AFDD algorithm from wrong decision (hoax faults)
- Diagnostic mapping tableMeasurements → Observations → States → Faults
Fault aggregation- Fault aggregation Fault relevance (of each fault) is updated in time