your data can stop leaks
DESCRIPTION
TRANSCRIPT
water network monitoring
Your data can stop leaks
February 2012 Haggai Scolnicov, CTO
Simple principle – complex reality
• Active Leakage Control reduces NRW (but not perfect)
• Simple principle (ideal world):Leaks cause a flow increase (+ some other anomalies)
So… Can I hook up my flow meter to the repair crew’s pager?
• Complex in practice (real world):– Fixed thresholds? Risk false positives or no detection!– Too many things look like leaks– You can’t even trust the data!
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Your data CAN’T stop leaks (alone)
Data quality
Other network events
Complex utility
process
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Let’s revisit Active Leakage Control
DMAsFlow
meters at inlets
Data analysis
and targeting
Field surveys Repairs
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Let’s revisit Active Leakage Control
DataContinuous monitoring
Data analysis
and targeting
FieldTriggered by
specific events
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13.58.9
7.2
4.7
9.1
Data analysis
and targeting
Let’s revisit Active Leakage Control
DataContinuous monitoring
Data analysis
and targeting
FieldTriggered by
specific events
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13.58.9
7.2
4.7
9.1
Data analysis
and targeting
Flow, GIS, calendar, network operations, pressure, weather, schematics…
Early repairsLess visible burstsContinuous serviceCost and capacity
• Sifting:check all data for all DMAs
• Statistical estimation:is flow surprisingly high?
• Special knowledge:was it caused by something else?
Computers are helpful with processing complex data!
Analyst = Superman?
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TaKaDu boosts the ALC process
TaKaDu’s unique anomaly detection algorithmsboost the data analysis phase of ALC in placeswhere algorithms best complement human insight
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Short commercial break - TaKaDu
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Better data analysis boosts ALC (cheaply!)
TaKaDu’s customers report, for example
• The same leaks are detected days to weeks earlier leading to less NRW, cheaper repairs, less visible bursts…
• Much less sifting and more reliable targetingTwice as much leakage fixed per hour in the field(e.g. because less “dry holes”)
Quantifiable savings over “standard” ALC… And a significant drop in the Economic Level of Leakage
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Your data CAN stop leaks, if you…• Know your data
• How reliable and what it means• How to handle wrong data• How to improve data infrastructure
• Know you network• What else is going on, that may be misleading• What else is going on, that may degrade data
• Know your process• What goals should ALC achieve• What tools are available for that (post-targeting)
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Data quality
Other network events
Complex utility
process
Know your data• What are the “measured” values?
• What is the sampling error?
• What about “rare” failure modes (drift, spikes…)?
• What is the sensor range?
• Data gaps and “filled in data”
• What is the timestamp (and how does it go wrong)?
• Context: location, DMA flow formula…
• And don’t get me started on GIS and workforce!
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Know your network
• Networks have a complex routine:• What is the consumption of a residential DMA, with
gardens and pools, on a warm Tuesday morning?• Which London DMAs consume water differently on
Ramadan? Or during the Olympics?• And then pressure management, reservoir control…
• … And even more complex anomalies:• DMA breaches and data faults pretending to be leaks• Network operations hiding concurrent leaks• And a whole lot of “background noise” messing up
your statistics13
Statistics take the edge off not knowing
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DMA 1
DMA 3
DMA X
DMA 6Strong correlation
Weak correlation
Statistics take the edge off not knowing
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DMA 1
DMA 3
DMA X
DMA 6Strong correlation
Weak correlation
Smart analysis sometimes makes up for missing information.For example, correlations in consumption patterns help distinguish
a global anomaly (e.g. weather) from a local one (such as a leak)
Know your process
• What is the goal of analysis?(Not as obvious as it sounds!)If you want few false positives, that may be very different than if you want early detection
• So you detected a leak – now what?How much does the field survey cost? Can you locate a very small leak? Should you?Is cost even the limiting factor? It could be maximum survey capacity, for example.
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Evolve your process
• Reliable detection changes the economics of ALC,e.g. trigger more acoustic surveys following alerts
• How to prioritise leaks for action?Leak rate, burst-prone areas, multiple adjacent leaks…
• Repair verification:tiny leaks, multiple leaks, and unsuccessful repairs
• Insight from evidence-based record of leakage: “problem areas”, longevity of different asset types, performance of ALC teams and methods…
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Network monitoring is more than ALC• ALC is just one part of network monitoring
• Other monitoring less developed, but just as valuable• Non-leakage events are evident in “leakage data”
• Monitoring non-leakage events helps ALC• Classify non-leak anomalies so they do not mislead• Alert on faults which cause bursts (e.g. high pressure)• Improve data by finding sensor faults, DMA breaches…
• Monitoring is much more than detection• Accurate description and measurement for targeting• Event tracking after detection, until verifying a repair
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Final thoughts
• Process integrationEarlier leak detection is worthless if neglected, or if leaks are too small for utility’s field detection tools
• Statistical performance is key – and hard to pin down!
• Real-world data “technicalities” are in fact the main challenge for data-driven ALC
• Mix of data analytics and domain-specific knowledge can deliver a powerful solution
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