crc-p smart linings session 3: for pipe and infrastructure

CRC-P – Smart Linings

for Pipe and Infrastructure

Project

24 March 2021

Session 3:

Water Code of Practice,

Sensors & Robotics

This presentation is for individual reference only, do not reproduce materials or publish without permission from WSAA.

CRC-P: Sub-Project 3

Smart Sensing and Application – Water Pipe Linings

Prof. Sarath KodagodaDirector (Acting) UTS Robotics Institute,

iPipes Lab,University of Technology Sydney Australia

24th March 2021

UTS Robotics Institute: water/wastewater

WSAA and partners

Robotic tools for water pipes

Robotic tools for wastewater pipes

• Non-traversable sewers 900mm-

1500mm

• Deployment through a 600mm

diameter manhole

• Expand to different pipe sizes

• Non-destructively assess the intact

concrete cover

• Online real colour three dimensional

view

• Data visualization to make online

decisions

Robotic tools for wastewater pipes

• 2020 AWA national winners, Research Innovation category

• 2020 AWA NSW winners, Research Innovation category

CRC-P Sub-project 3: Smart Sensing and Application

Water Pipe Lining Infrastructure

• Sub-project 3 focuses on the development of sensing tools and deployment strategies

Post-application quality assurance (PAQA)

Long-term performance monitoring (LTPM)

• PAQA: Sets a benchmark for the applicators to deliver the specified liners and enables the

utilities to be confident about the application of products.

• LTPM: Enable utilities to forecast timely repairs to their assets.

CRC-P Sub-project 3: Smart Sensing and Application

Milestones

1. Identification of product-specific defect parameters to be monitored in PAQA and LTPM.

2. Development of an easy to use real-time tool to non-destructively assess the applicationquality and performance of linings.

3. Development of sensor deployment strategies, signal transmission techniques and real-time asset management tools for liner monitoring.

4. Feasibility study on liner embedded sensors.

5. Validate the prototype sensors and robot through lab tests, test beds and field trials

Linings for Water Pipe Infrastructure

Lining Types Product and Manufacturer Test site

Cured In-place Pipe (CIPP) Lining

AquapipeFrom

Sanexen

Sydney Water site–Strathfield testbed at

Sydney

Polymeric Spray Lining

Subcote FLP from

Radius Subterra

Sydney Water site –Strathfield testbed at

Sydney

Identification of Product Specific Defect Parameters

Survey Study

• UTS conducted a survey study to identify the most important product specific defectsparameters to be monitored for PAQA and LTPM of water pipe linings.

• 21 members took part in the survey that includes water utilities, lining manufacturersand applicators, researchers and water associations.

Linings PAQA LTPM Sensor Technology

CIPPLiner

Imperfections**Liner

Imperfections**Laser scanning - Robotic

SprayLiner

Imperfections** & uneven thickness

Liner Imperfections** & uneven thickness

Laser scanning, Ultrasound-Robotic

**Liner imperfections: folds, wrinkles, dimples, bulges, sagging, liner peeling (de-bonding), tears, damages and blisters

Mini Pipeline Inspection Robot – (mini-PIRO)

Features: On-board Sensors

• CCTV – real-time monitoring

• Laser profiler – building 3D pipe map

• IR and RGB camera – to fuse real-time colour information in 3D pipe map

• Ultrasound sensor – uneven thickness monitoring of spray linings

• Wheel encoders – to know the distance travelled by the mini-PIRO mini-PIRO was developed under the Sydney Water funded project,

"Development of sensor suites and robotic deployment strategies for condition assessment of concrete sewer walls" is an in-kind contribution to this CRC-P.

Using Cameras and Laser Profiler to build 3D Pipeline Map

Laser profiling demo

Laboratory Testing: Liner Imperfections of Spray and CIPP Linings

Artifacts for Sensing Evaluation

• The purpose of these experiments was tovalidate the accuracy of 3D map measurements,RGB depth mapping, defects mapping, andorientation detection.

• Artifacts with known dimensions were attachedto the internal pipe surface to validate the sensormeasurements.

• Different color stripes (red, green, and blue)were placed on the pipe surface to validate thecolor alignment.


LocationPhysical

measurement(mm)

Point cloud measurement

(mm)

Error(mm)

Pipe diameter 445 445.36 0.36Point 1 13 13.56 0.56Point 2 6 6.05 0.05Point 3 1 1.68 0.68Point 4 14 31.15 17.15

Liner Imperfection: Defect Size Validations

Location(Fig. 6a)

Physical measurement – Vernier Caliper

(mm)

Point cloud measurement

(mm)

Error(mm)

Right Defect Height Length

110110

111.31109.72

1.312.78

Left Defect Height Length

110110

107.87115.16

2.135.16

Lab setup Unwrapped 3D point cloud


unwrapped point cloud with the defects heat map for the lab setup

Lab setup 3D point cloud


Further Lab Validations

• In addition to the previously mentioned labexperiments, we used a DN600 corroded metalpipe extracted from the Sydney Water networkto perform further tests.

• We scanned the pipe using a commerciallyavailable highly accurate (0.1mm accuracy) 3Dscanner "Creaform EXAscan SYS-H3D-EXAD".This 3D scan is benchmark.

• Mini-PRIO system scans were compared withthe benchmark. Results indicate qualitativelysimilar scan were produced by the mini-PIRO. Benchmark scans mini-PIRO scans

GUI for Evaluation of Liner Imperfections – Spray & CIPP Linings

Colour Image

Unwrapped pipe

view (colour map

represents areas

away from an

ideal cylinder)

Projected laser

light

Slider: Moves the

images and views

IR image

Actual laser light

Frames with pipe

distance

GUI for Evaluation of Liner Imperfections – Spray & CIPP Linings

Ultrasonic Uneven Thickness Sensing of Spray Linings

Operation

• Ultrasound sensors emit pulses in the form of ultrasound wave signals at a particularfrequency. With appropriate coupling, they can penetrate materials and bounce back.

• Spray lining thickness is dependent on the spray lining material propagation speed (𝑣) and thetime taken (τ) by the ultrasound wave signals to pass from the material’s surface to its otherboundary layer.

• With known spray lining material thickness (Mᴷ), the propagation speed (𝑣) is given by

𝑣 =2 x Mᴷ

τ

• Once the propagation speed (𝑣) of the spray lining material is determined, the unknown spraylining material thickness (Mᵁ)

Mᵁ = 𝑣 xτ

2


Sensing System Electronics

• (a) Ultrasound pulser board, (b) Data acquisition unitand, (c) Ultrasound transducer.

• An ultrasound pulser board capable of generatinghigh-frequency ultrasound wave signals.

• Data acquisition board (with an ADC differential inputwith a resolution of 14 bits and a speed of 100 Msps),which are housed in the sensor electronics unit.

• Tests were conducted to identify the most appropriateultrasound frequency.


Sensor Calibration

• Propagation speed of ultrasound wavesvaries depending on the material

• We have fabricated a test piece sample with athickness of 8 mm to determine propagationspeed for the spray lining material.

• The peak signal seen at 16.20 μs is from thetop surface whereas the peak at 23.64 μs isfrom the bottom surface of the test sample.

• The time difference in between the peaksignals is the signal travel 7.44 μs.

• Propagation speed is 2150 m/s


Lab Testing Sample

• A laboratory test sample was made byapplying the spray lining material over a flatmetal sheet.

• This test piece has a varying thickness of spraylining across its area.

• In the centre of the test piece, a grid of 2 rowsand 24 columns were drawn.


Lab Testing – Benchmark Measurements

• Scanned with a highly precise handheld laserscanning device (EXAscan 3D Scanner,Creaform) with a resolution of 0.05 mm beforeand after the spray lining was applied.

• The difference between the two 3D scansenabled us to construct a new 3D model thathas accurate thickness information on theapplied spray linings.

• New 3D model was used as a benchmark totest the thickness measurements of theultrasound sensor.

Validation: Uneven Thickness Sensing of Spray Linings

Sensing Locations Mean Absolute Error Root Mean Square ErrorRow 1 0.11 mm 0.34 mmRow 2 0.16 mm 0.40 mm

Overall 0.14 mm 0.37 mm

Acoustic Coupling Mechanism for Continuous Measurements

Operation

• Important for mini-PIRO toperform continuous sensing.

• Requires a built-in couplingsystem connected to theultrasound probe, which isrealised through a spring-loaded water spraying systemproviding continuous surfacewetting for the pipe liner wall.

• Ultrasound sensor was fitted ina watertight enclosure with anelastomer coupling pad on thesensor head.

Acoustic Coupling Mechanism for Continuous Measurements

Operation

• The coupling agent in this work is water, which isstored in a small reservoir fitted with a pump that iscontinuously in operation.

• Continuous acoustic coupling probe mechanism wastested inside the lab water pipe (PVC made).

• Acoustic coupling system consists of the waterreservoir, the water pump, and the tubes that supplywater.

• mini-PIRO traversed through the pipe whilecontinuously taking measurements without anydrop of signals reinforcing the effectiveness of thecoupling mechanism.

Data Visualization: Spray Lining Uneven Thickness

Overview

• A tablet computer is used in the remote station to visualise the ultrasound sensor readings in real-time as the mini-PIRO traverses inside the pipeline.

• The mini-PIRO transfers sensor measurements to the cable drum located in the pipe pit via a tether cable, from where the sensor data is transmitted to the tablet through WiFi signals.

• The tablet runs on a ROS (Robot Operating System) middleware and it is built with custom-developed programs for ultrasound sensor data visualisation.

GUI for Spray Lining Uneven Thickness Monitoring

mini-PIRO Loading Tests using Winch Mechanism at Lab

Field Deployment Mock Testing (February 2019)

• In the very first run in field, robot traversed 105 meters.

• Data collected in first 60m

Field Deployment and Evaluations – Spray Linings (May 2019)

Field Evaluation: Liner Imperfections – Spray Linings

Pipe Details

• Strathfield Testbed (Sydney Water) Cast iron cement lined pipe with 580mm internal diameter

Field Evaluation: Spray Linings Uneven Thickness

Ultrasound video

Field Evaluation: Spray Linings Uneven Thickness

Ultrasonic Robotic Sensing

• The mini-PIRO continuously took ultrasound measurements in the crown region (12 o’clock position) of the pipe.

• The thickness of the spray lining is about 3.7 mm from 1.8 m to 3.5 m and about 7 mm from 3.5 m to 36 m. This is inline with the spray liner applicator specification.

• The high frequency noise in the data is mainly attributed to the presence of minor ripples observed through CCTV.

Thank You Everyone for Listening!

QUESTIONS?

Contact details:

[email protected]

0409 333 540

Additional Information:

https://waterportal.com.au/smartlinings/

mailto:[email protected]

https://waterportal.com.au/smartlinings/

crc-p smart linings session 3: for pipe and infrastructure

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