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Your data can stop leaks
Your data can stop leaks
Your data can stop leaks
Your data can stop leaks
Your data can stop leaks
Your data can stop leaks
Your data can stop leaks
Your data can stop leaks
Your data can stop leaks
Your data can stop leaks
Your data can stop leaks
Your data can stop leaks
Your data can stop leaks
Your data can stop leaks
Your data can stop leaks
Your data can stop leaks
Your data can stop leaks
Your data can stop leaks
Your data can stop leaks
Your data can stop leaks
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Your data can stop leaks

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  • 1. water network monitoringYour data can stop leaksFebruary 2012 Haggai Scolnicov, CTO
  • 2. 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! 2
  • 3. Your data CAN’T stop leaks (alone) Other Complex Data network utility quality events process 3
  • 4. Let’s revisit Active Leakage Control Data Flow analysis Field DMAs meters at Repairs and surveys inlets targeting 4
  • 5. Let’s revisit Active Leakage Control Data Data Field Data analysis analysis and and Triggered byContinuous monitoring targeting specific events targeting 5
  • 6. Let’s revisit Active Leakage Control Data Data FieldFlow, GIS,Data calendar, analysis analysis Early repairsnetwork operations, and and Triggered by Less visible bursts Continuous monitoring targeting specific eventspressure, weather, targeting Continuous serviceschematics… Cost and capacity 6
  • 7. Analyst = Superman?• 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! 7
  • 8. TaKaDu boosts the ALC processTaKaDu’s unique anomaly detection algorithmsboost the data analysis phase of ALC in placeswhere algorithms best complement human insight 8
  • 9. Short commercial break - TaKaDu 9
  • 10. 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 targeting Twice 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 10
  • 11. Your data CAN stop leaks, if you…• Know your data Other Complex Data • How reliable and what it means quality network events utility process • 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) 11
  • 12. 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! 12
  • 13. 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 statistics 13
  • 14. Statistics take the edge off not knowing Strong correlation DMA 1 DMA 6 DMA X Weak correlation DMA 3 14
  • 15. Statistics take the edge off not knowing Strong correlation DMA 1 DMA 6 DMA X Weak correlation Smart analysis sometimes makes up for missing information.For example, correlations in consumption patterns help distinguish DMA 3 a global anomaly (e.g. weather) from a local one (such as a leak) 15
  • 16. 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. 16
  • 17. 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… 17
  • 18. 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 18
  • 19. Final thoughts• Process integration Earlier 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 19
  • 20. water network monitoring Thank you Haggai Scolnicov haggai@takadu.com

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