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Smart Water Network and IoT Systems
AdelaideSouth Australia
Dr Mark StephensPage heading goes here

Example Value Proposition:
Acoustic Alerts for Proactive Main
Break Repair

Adelaide CBD Water Network IoT – why and what?

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Transient/hydrophone logger

Water quality logger

Acoustic (accelerometer) loggers

Smart customer meter

IoT framework

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LPWan

Operational
Data Store(ODS)

Acoustics
3G/4G

Smart Meters

MicroSpiders

Water Quality

Enterprise Data
Warehouse(EDW)

Microsoft BI

GeospatialServices

Smart Water
Analytics

Smart Water
AnalyticsMachine Learning

Smart Water
Analytics

Detecting main breaks

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ANALYSIS

Proactive
Repairs toPrevent
Uncontrolled
Main Breaks

The sensor network: current and future

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Adelaide CBD Smart Network Sensors

North Adelaide Smart Network Sensors

300 active acoustic loggers in the Adelaide CBD.

Out of 126km of pipes, loggers concentrate on those with a higher failure risk and impact.

Understanding environmental noise

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We hear many forms of significant and ongoing environmental noise.
Understanding what ‘typical’ acoustic signatures look like, and noises sound like, is key.

Identifying faults

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Fault patterns and features are being identified, including for cracked pipes, leaking
valves, leaking water meters and/or leaks on the customer side of water meters.
‘Faults of interest’ are based on noise:• Persistency• Magnitude• Frequency• Changes• Rate of change
Circumferential Crack

Longitudinal Crack

Type and status of a crack can now be identified in a number of cases.
Helps us assign the urgency for repairing it.

Circumferential cracks

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Typical: significant initial magnitude which remains
relatively stable until repair.

Longitudinal cracks

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Typical: small initial magnitude which increases in an
unstable manner until repair.

Proactive versus reactive repairs

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Third party services

Completed withless cost andcustomer
disruption.

Proactive night repair King William

Reactive day repair Grenfell St

Enable third party
infrastructure tobe bettermanaged.

July 2017 toMarch 201933 North Adelaide

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30

127

1416

336 reactiverepairs

219 1835 31 10393541610625

82172221328

1 383420 2926242

40 proactive
repairs

24614211927 25423

152037 29

9 18171122 8 121523

7732

26

51113436

151228630

213 940

Based on an
audit of all 76
events there
has been a 24%
repair cost and35% supply
interruption
time reductions
for theproactive
repairs.

July 2017 toMarch 201930

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19 1835 31 1039

625
20 29
268242

28

38

Total 76 repairs

324541610127

1416

321722213614211927 25

23152037 29

9 18171122 8 121523726
73230

213 940410 year main
break heat
map

34436151228

511613

Results – main breaks

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Ten year main breakaverage for the Adelaide
CBD varies from four –five per month.

Total main breaks since 1
July 2017 follows the tenyear main break rate.
Proactive repairs(currently 52 per cent, atend March 2019) exceed
reactive.

More data & analytics?

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Future goals:• more accurate alerts (in terms of leaklocation and identification of fault type)• extending sensor cover and optimising
placement (system risk profile)• issue alerts as near to real time as
possible (by separating environmentalnoise)

Improvements:• fully test next generation sensors/DAQ• complete noise filtering systems• complete fully automated analytic
algorithms

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IoT (Internet of Things)
Acoustic Alerts Analytics

Noise Classification – Leaks (and non-leaks)

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In the case of the SA Water
Adelaide CBD Smart Water
Network, over 200,000 noise
files have been captured.

Many examples of noises
not associated withcracked pipes areavailable for training.
Similarly, many examples ofnoises that are associatedwith cracked pipes areavailable for training.

Noise Classification – Feature Identification

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Customisedfeatures havebeen identified in
the noise which
are indicative ofleaks and otherforms of noise.

Power Density

Number of Peaks

Local Peak

Bandwidth

Frequency

Bandwidth

Median Frequency

First Dominant Frequency

First Dominant Frequency

Features include:•Number ofpeaks•

Bandwidths•

Medianfrequency (MF)No leak noise
Leak noiseFirst Dominant Frequency

Classifier – Machine Learning the Noises

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Classification typemachine learning has
been adapted toenable training to key
features from cracked
pipe and non-cracked
pipe noises.

Results of Training (and then Testing)

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The left hand side confusion map shows a 90.7% true leak and 98.5% true no-leaktraining outcome.
The right hand side confusion map shows a 74.5% true leak and 96.1% true no-leak test
outcome using the training learner.

Custom Coded ANN for Cracked Pipe Detection

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Input layer

…

……

…

Hour of Day
associated withthe horizon tobe predicted……

…

Day of Week
associated withthe horizon to
be predicted……

…

Power density
level of theprevious 24windows(giving f up to2400Hz)

Hidden layer

Output layer

Matlab, R andPython scripted
Artificial Neural
Networks (ANNs)
have been used toanalyse noise
magnitude andfrequency data.

Custom Coded ANN – Using PSD Patterns

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The application of
the previous custom
coded ANN to aseries of PowerSpectrum Density(PSD) measurements(for a particularlogger).
Bands areestablished for the
expected behaviourand deviation fromthem can indicate anew leak.

Custom Coded ANN – Using Magnitude Data

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Modification of thecustomised ANN
enables it to be
trained to historicalnoise magnitude
data.
Deviations from theANN predicted
pattern can indicate
a new leak.

New Sensor Based Analysis - Filtering

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Distinct from the analytics illustrated above, sensor based filtering of noise
measurements using historical data, at either individual loggers or across groups ofloggers, is undertaken to identify initial noise level changes and any on-going change.

Leak Identification – Persistence Spectrums

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The occurrence of a leak (and other noise sources) can also be detected using
persistence spectrums (for the day 1, 2 and 3 noise measurements presented on
the previous slide).

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SCADA and IoT – PI and Azure

Current Primary SA Water Data Storage Structure

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The SA Water main
data wharehouses
include an EDS andEDW which become
accessible to all SA
Water users.
SCADA data can be directedfrom Historian to a SA Water
Operational Data Store (ODS)
and then extracted to the EDS
from the ODS (see below).

Developing Data Framework(s)Page heading goes here

SA Water is awareof changes to dataand information
frameworks.
Within theframework shown
IoT stream(calculation level )and morecomplicatedmathematicalanalysis of data
can beundertaken.

Developing Data Framework(s)Page heading goes here

Data andinformation
framework
including Azure
machinelearning
environment(and dataexchange intoand out of it).

Analytics in OSI Soft PI and Azure

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One version (high level)of an operationalisation
pathway for OSIsoft PI
data (from SCADA)
through Azure of
relatively more complex
analytics (includingmachine learning).
SA Water has
implemented a similar
pathway for IoT data inAzure but not fromOSIsoft PI (yet).

Framework for use of SCADA and IoT Data

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SCADA

Alarms/Control

Data
Sensors

Site Based
Analytics (PLC)

Historian

ODS – linear and timestamped only
GIS/Maximo

EDS DB (SA Water)

Azure

Mongo DB(SA Water/Visenti)

CitrixPI Link – to AF, EV and VISION

IoT

Von Roll and other IoT FTPs

SCADA and IoT – Port Lincoln SA

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A combination of new (IoT)
and existing (SCADA) flow andother sensors being used
together.
Objectives include thecreation of virtual meteredareas for the assessment ofwater loss and quality.
Delivery efficiency improvedby utilising new (IoT) andexisting (SCADA) sensors andsystems.

Port Lincoln Township Flow, Tank Level and Water Quality Sensors (new ingreen/red and existing in grey)

SCADA and (to) IoT – Port Lincoln SA

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Visualisation offlow data fromdifferent IoT(green) andSCADA (blue)
sensors.

Data able to be
directed toanalytics forflow balanceand loss
determination.

Penneshaw Smart Meters

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300 smart meters have been installed on
all customer connections in the townshipof Penneshaw SA.

These smart meters transmit consumption
data back to SA Water’s IoT analytics
platform via Taggle radio
communication infrastructure.
A mixture of IoT and SCADA flow andtank level information is also transmittedback to the IoT analytics platform wherea range of native flow balancing
calculations are undertaken.

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Operationalising Alert Analytics

Implementing Analytics

SA Water has not yet established an IT
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framework in which relatively more complex

analytics can be executed with SCADA data.

C and/or Fortran

Statisticalprogramming

However, SA Water believe that the IoT
analytics it has developed andoperationalised in Azure will be able to bemodified for use with OSIsoft PI (if useful).

Azure Operationalisation

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SA Water has operationalised a number ofanalytics involving relatively more complex
mathematics:•••••

iterative solving
matrix handling
integration/differentiationtransforms (e.g., Fourier)customised and classification basedmachine learning)in the Azure Environment (Databricks) using R
and Python program scripts.
It is anticipated that similar analytics can becreated for application to SCADA data.

Azure Operationalisation continued ….

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Output Data
Storage in SA Water
EDS SQL Database

Storage inAzure Blob

Visualisationof Results inPower BI

AnalyticProcessing(Custom Scripts) in
Azure Databricks
Data Ingestion
from SA Water
EDS

Customised test operationalisation of SA Water developed noise magnitude change
detection analytics and visualisation in Power BI.

Operationalisation – Alert Dashboards

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The alerts(based onchanges inmagnitude andfrequency data)
are transferred
to customised
dashboards for
24/7 operationalmonitoring.

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Future Direction for SA Water

Other Analytics – Current and Future?

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SA Water are current expanding its
Acoustic
Leak
Detection

EnergyManagementNetworkCalming

Plant
Performance

Data SystemsandAnalytics
Machine Learning

Wastewater
Overflowsand Odour

Trunk Main
Monitoring

application of data analytics andmachine learning in many areas.

VirtualSensors

SmartMeters

Stream or calculation type analytics havebeen/are being implemented in someareas (e.g., threshold setting, bands,
basic mathematical operations, thresholdsetting, mean and SD, bands, limited
data call back).
More complex analytics have been/arebeing implemented in other areas (e.g.,
iterations, matrix solving, statisticalanalysis, transforms, spectral analysis
through to machine learning).

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Thank you

Contact me via email:mark.stephens@sawater.com.au