Development of an Online Expert Rule based Automated Fault Detection
and Diagnostic (AFDD) tool for Air Handling Units: Beta Test Results
ICEBO 2013, MONTREAL, CANADA
•
Why does industry need this work?
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Why HVAC?
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An automated solution…..
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AFDD techniques
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Early Results
Agenda
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Focus on production output in lieu of efficiency
•
Difficult to track efficiency due to islands of information
Energy Consumption remains relatively constant
though production volume has decreased
significantly
•
Typically greater than 20% of an industrial site’s energy consumption
•
HVAC systems get less efficient over time
•
20‐30% energy savings are achievable by re‐commissioning HVAC
0
5
10
15
20
25
30
35
Manufacturing
HVAC
CHW
LTHW
Others
Operation & Management Control Process Equipment Energy Service Requirement
How can AFDD help save HVAC Energy?
Operation & Management Control Process Equipment Energy Service Requirement
How can AFDD help save HVAC Energy?
Air Change Reduction
Operation & Management Control Process Equipment Energy Service Requirement
How can AFDD help save HVAC Energy?
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Inefficient control strategies
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Poor loop tuning
•
Free cooling
•
Incorporation of Deadbands
•
Simultaneous heating &
cooling
•
Set point adjustments
•
Manual operation
•
Sub optimal performance of
equipment
•
FDD automates the process of detecting and diagnosing faults
•
A rule based FDD tool can be developed and implemented in
industry relatively quickly utilising existing equipment
FDD Methods
Quantitative Model
Based
Detailed
Physical Models
Simplified
Physical Models
Qualitative
Model Based
Rule Based
Qualitative
Physics Based
Process History
Based
Black Box
Gray Box
FDD Techniques
•
Business layer expanded on existing knowledge based rule sets by;
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Applicable fresh air & recirculation AHU’s as well as return air units
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Detecting issues when the AHU is off
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Calculating virtualised readings
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Improved error threshold calculation
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A server side application performs the mode checks, calculates the virtual values,
applies the business layer rules, and stores the results in the database
The Business Layer
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> 200 AHU’s available on 7 industrial and commercial sites
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AHU’s were selected with
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Different component and sensor layouts
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Varying levels of instrumentation
Design of Test Study – AHU Selection
Site
No. AHUs
in pilot
Type
Type
of
zone(s)
supplied
Operating
hours
per
annum
BMS/Data
logging
Platform
Frequency of
logged data
1
2
Constant volume
Office & canteen
8760
Trend
15 minutes
2
4
Constant volume
Manufacturing Floor
8760
Tridium
15 minutes
3
9
Variable Volume
Manufacturing Floor
6240
Cylon
15 minutes
4
4
Constant Volume
Commercial
office
space
6240
Cylon
7.5 minutes
5
3
Variable Volume
Commercial
office
space
6240
Schneider
15 minutes
Alpha to Beta Testing
63 2012‐08‐18 18:15 Sat19 Hour 13 Day 2 Week 0 TRUE TRUETRUE TRUE Zone
TRUE V V V V V + ‐ 11.4°C 21.7°C 59% Fault selection 24.8°C 25.8°C 18.9°C 18.7°C 9.2°C List faults by… 10.0°C 10.4°C Return air Supply air Rise1.0°C 0% Rise1.0°C 100% 100% Faults in this hour: 10.0°C OutOfControl_zonT_Hi, ComponentPosition_AtCapa city_cooV site3 : ahu9
Exhaust air Outside airMean Weather
Temps Humidities Virtual value
Other cond ComponentPosition_AtCapacity_coo Valve/damper position …frequency …cost Reset schematic diagram Browser based GUI Development of web based configuration tool Secure HTTPS communication
