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

0.2

0.25

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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