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Online data driven burst detection
Online data driven burst detection
Professor Joby Boxall
Pennine Water Group
University of Sheffield
Professor Joby Boxall
Pennine Water Group
University of Sheffield
Winner of IWEX (Sustainabilitylive!)University Challenge 2010
Leak detection: current practice
24 h rs
Flow
Source
RawWater
Transfer
TreatmentWorks
TrunkMains
Distribution ManagementArea
DistributionSystem
Industry
Meter Sites
ServiceReservoir
Data and SensorsImproving technology, and cost reductions associated with communications, is resulting in more data from increasing numbers of sensors Current mechanisms for detecting events in the control room include ‘flat line’ alarm levels on key monitoring sites as well as nightline analysis Regular automated data analysis can identify new leaks as they occur, including those not displaying surface signs - intelligent ‘smart alarms’
YWS RTNet pilotYWS RTNet initiative - GPRS enabled flow and pressure devicesProvides near real time data as time series
logging every 15 minstransmitting data every 30 minsFlat line analysis system
Harrogate and Dales made available as basis for project Neptune200 DMAs available since the start of 2008
Cello loggers
Artificial Intelligence detection system: concept
An automated online analysis system, based on Artificial Neural Network and Fuzzy Logic technology, for the detection and size estimation of leak/burst eventsThe approach is useful for detecting individual burst events from District Meter Area (DMA)Capable of detecting around 2-5%of maximum DMA flow
YW
Emergency Response
Alert
Data Processing
YW
LEAKAGE
Life cycle of a burst
SOURCE: Adapted from WRc 1999A
Artificial Neural NetworksANNs are parallel computational models consisting of densely interconnected adaptive (through learning) processing unitsMany real world applications e.g. Pattern classification, speech recognition, forecasting and prediction etc.For this system, Mixture Density ANN trained on historical data Can then predict conditional density function of the target data for given value of input vector
Fuzzy Inference System Fuzzy Logic represents the impreciseness of human reasoningFuzzy sets contain elements with partial degrees of membership (somewhere between 0 and 1) Output of the MDN ANN is used to construct user modifiable confidence levels for classification in Fuzzy LogicA Fuzzy Inference System provides levels of confidence of ‘burst’ for a window of readings Output meaningful to human operator
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FIS
outp
ut
ANN / Fuzzy Logic System
Pre-processing
DB
Sensor
Post-processing Statistics & predictions
FIS Classification and size estimation Module
Mixture Models
MDN ANNs
CSV/DMGPRS
Self-learning modelFlow into
DMA
Time
Actual Flow
Flatline Alarm
Actual Values
24h predicted
Normal (Gaussian) Distribution
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0.05
0.1
0.15
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Probability Density
Normal (Gaussian) Distribution
GPRS
COMMSDATA WAREHOUSE
AI SYSTEM
ODBC
CSV
LOGGER
ALERTS DB
ALERT !
HOURLY ANALYSIS
Detail: Logger, DMA, time, size estimate, % confidence
ODBC over FTP
YWS CONTROL
FTP
Live Pilot200 DMAs
RTnet alarm level
*ALERT* DMA E071 Knaresborough TownBurst size = 0.7 l/sCONFIDENCE RATING: 99.7%
Harrogate & Dales ‐ RTnetNear real time notification of major bursts AI ‐ Analysis / alerts of DMA leaks / burstsNear real time dataArtificial Intelligence ‐ Black BoxAlert sent to Control RoomEquivalent to 4 barrel tankers / dayExample alertBurst occursAI AlertCustomer Contact: Burst / LeakCustomer Contact: Burst / LeakRepair Completed
Event detailsEmail received 15:15 13/12:
DMA: E016 *ALERT* %CONFIDENCE: 95.00Dates:13-Dec 00:30 To 13-Dec 12:30Burst Size estimate: 0.4 ls/
Domestic properties 124In this case, the AI system gained more than two and a half days in detection time over the customer contact.
XXX
XXX
X
Event detailsEmail received 06:00 25/11
DMA: E070 *ALERT* %CONFIDENCE: 85.00 Dates: 23-Nov 23:00 to 24-Nov 11:00Burst Size estimate: 0.336
Domestic 96 Commercial 7No RTNet alarm reported, customer burst contact but no WMS information
AIAlert 4:12am
Email received 04:12 21/2 DMA: E091 *ALERT* %CONFIDENCE: 80.00 Dates: From 20-Feb 15:00 To 21-Feb 03:00 Burst Size estimate: 0.42
No contacts, WMS or other information, However, night line raised by approx 0.5 l/s
Flatline alarm level
Lee Soady starts hydrant flush(2 l/s?)
End of flush
~1.5 l/s
AIAlert00:13amDMA: E023
*FLOW ALERT* %CONFIDENCE: 80.00 Fuzzy output: 0.88 Dates: From 03-Mar-2010 11:30:00 To 03-Mar-2010 23:30:00 Burst Size estimate: 1.4Alert written to AAR on iNeS.
‘Blind’ EE
Engineered Events:blind tests
DMA Size Hydrant opened at
Hydrant closed at
Alerts
E021 2 l/s 09:101/3/10
07:502/310
Flow E021 13:11 2/3/101.4 l/s
E023 2 l/s 07:253/3/10
07:254/3/10
Flow E023 00:13 4/3/101.4 l/s
E021 2 l/s 07:2015/3/10
07:1516/3/10
Flow E021 06:22 16/32.3 l/s
E020 2 l/s 07:4016/3/10
07:1517/3/10
Pressure E020 07:20 17/3No F available
E204 2 l/s 07:3517/3/10
07:1518/3/10
Flow E204 21:21 17/36.9 l/s (nightline agrees)Flow E026 – cascadedPressure E0204 DG2 01:22 18/3
E022 2 l/s 07:2518/3/10
07:1519/3/10
No Alerts – system retraining
Final online trial Jan - March 2010 227 flow (78) and pressure (149) alerts
AI alerts (overall, first quarter 2010)
27%
39%
3%5%
4%
22%
Ghost
Abnormal
Engineered events
Low pressure /no watercontactBurst contact
Burst repair
Flow
5%
48%
6% 5%5%
31%
Ghost
Abnormal
Engineered events
Low pressure /no watercontactBurst contact
Burst repair
Pressure
38%34%
1%5%4%18%
Ghost
Abnormal
Engineered events
Low pressure /no watercontactBurst contact
Burst repair
47% of flow alerts corresponded to WMS/contact information or known engineered events with only 5% ghosts.
KTP with YWS 2010-12DSS Environment• Risk-Based incident investigation (risk maps)•Intervention “What-If”scenario evaluation
Input to DSS
BenefitsANN/FIS system applied online and proven to accurately identify new leak/burst (and other) events as they occurLow number of ghosts vs. genuine event detections, especially for flowProvides a confidence estimate of the abnormality of the flow and an estimate of the event sizeComplementary to flat line system for catastrophic events
Ability to detect medium to small events which a flat line system cannot
Potential to detect events before customer contactReduced ‘awareness’ period
Detection of different abnormal events, not only leaks / bursts
AcknowledgementsDr Steve MounceYorkshire Water Services – Project ADAEPSRC (and all partners)– Project Neptune
Water quality monitoringInstrumentation for multi-parameter water quality measurements e.g. Intellisonde: Flow, Pressure, Temperature, Total Chlorine, Dissolved Oxygen, pH, ORP, Conductivity, Colour and Turbidity. Can be connected via GPRSCan be indicator measurements e.g. surrogate parameters for contaminants (Hall et al. 2007)Interpreting data:
Conductivity Source of supply, pollution warningRedox DisinfectantTemperature Bacti, taste and odourpH Supply, treatment failureDissolved oxygen Stagnation, biofilm, pollutionChlorine Bacti, taste and odourTurbidity Discoloured and/or cloudy water
Other parameters such as TOC, Ammonium, Fluoride and Nitrate… Unique opportunities afforded by combining hydraulic, WQ measurements and asset information.
A major burst occurred in the network as a result of structural failure of a six-inch cement lined main, repaired within 12 hrs.
EDS for quality data analysis – Multivariate Nearest Neighbour across multiple parameters
Current work
Future workData RichInformationPoor
Fixing the ‘DRIP’Data collection has been driven by regulationOperational and performance information required
Vast array of similar challenges and opportunities exist and are emerging
Next generation of (quality) sensor and communication technologiesApplications in sewerage (see this afternoon)Local versus central intelligenceCloud / grid computingUncertaintyDynamic conditions
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