SmartData

Briefing & Opportunities for the Telecommunications Industry

Pixeld — Thu, 17 Feb 2022 01:12:25 +0000

SUMMARY

Following on from a training and development package prepared and delivered for a major Telco provider Power, this document has been put together to help identify future opportu- nities for Remotely Piloted Aircraft (RPAS), in the Telecommunications sector. We aim to identify other cases around the world where efficiences have been realised by utilising RPAS technology to improve inspections and monitoring of towers.

One of our original trials for a large Telco client in Australia was utilising RPAS technology to help with defect identification of structures as well as cataloging of potential areas of interest for comparison of these issues over time. This trial leveraged a combination of manual review as well as deep learning methodologies for automated identification of defects and areas of interest.

OPPORTUNITIES

The opportunties for Drones in the Telco in- dustry are plentiful. There are situations where drones will allow data to be captured in a more efficient manner, saving time and resources that can be diverted elsewhere. This can be anything from asset inventory calculations, through to asset management and condition reporting, and antenna azimuth measure- ment, as well as RF measurements.

Drones can be used to improve the reliability of data supply through condition monitoring, and fault finding on areas where supply and faults may already be identified. There is a safety realization as elevated work platforms only need to be used for repairs/installations instead of inspections.

Leveraging additional technologies such as deep learning, and artificial intelligence will enable further savings, cost reductions and efficiencies to be realized, thereby enabling better outcomes for both customers, and Tel- co companies themselves. Lastly – telco pro- viders can utilize the data (in the form of 3D models, point clouds, and images) as a single source of truth for equipment which resides on a tower.

Another use case for Telcos is better under- standing additional space on their vertical assets for revenue generation – IE renting that space out to other providers and creating rev- enue. This will become particularly relevant as 5G is further introduced

REGULATIONS

One of the current challenges preventing Drones from being utilised more regularly in Telco applications, is the requirement in Aus- tralia to keep the RPA within line of sight of the person conducting the operations. Typically, this limits the radius of operations to 300-400 metres, depending on the person’s eyesight, and size of the RPA itself.

Future iterations of the regulations, namely the manual of standards will allow drones to be operated under what’s called extended visual line of sight (EVLOS)

The updated extended visual line of sight reg- ulations allow for the RPA to be operated up to 1500 metres away from the relevant observer. There are 2 classes of EVLOS operations pro- vided for under the Manual of Standards.

There are additional opportunity sets to be realised in Telco surveys allowing more auto- mation.

Drones have the ability to capture a higher quality dataset reapatably, generally with more information per image/scan than tradition- al methods. Valuable analytics can then be extracted from each asset, whether that be tower, mast, or other structure.

OPPORTUNITIES – TOWER INSPECTION PROGRAMS

In the past, inspection programs have been completed by riggers climbing towers, utilizing EWPs or cherry pickers to get the required inspections of towers. This is often a manual process with associated risk of people working at heights. This process is also largely subjective when it comes to data review, as one engineers view may differ from others. This is where deep learning assists decision making for telecommunication providers and infrastructure owners, as human bias can largely be removed from the model in a well trained dataset.

OPPORTUNITIES – DEEP LEARNING AND ARTIFICIAL INTELLIGENCE ANALYTICS

Where real cost savings and efficiences are able to be realised in Telco networks, is using deep-learning to identify and report on inidividual elements and record asset inventory. Deep learning is a subset of Artificial Intelligence.

Deep learning models perform well when the data used to train the model is of a high standard, and has been labelled correctly in the first instance. There are elements of deep learning models using re-en- forcement training among other things, but the first step to correctly deploying deep learning and data analytics is to ensure the data captured is of a sufficiently high quality. A poorly trained deep learning model will create additional challenges with false positives and incorrectly identified aspects requiring re-training.

By coupling automatic recognition of assets and inventory, as well as identification of defects on struc- tures, and repeatable flight paths, many of the challenges associated with manual inspections can be solved. Obviously large amounts of assets don’t lend themselves well to being reviewed by humans, when the datasets captured are quite large. Deep learning can be used to identify initial areas of inter- est, which can then be reviewed for accuracy.

The more data that is captured, and built, the more accurate the data becomes over time. Whilst the model is being trained initially, it is important to take a longer term view (1-5 years). A correctly trained model will be able to get to a high confidence interval.

The overall benefits mean that speed and quality of data capture is improved, as well as a reporting and analytics improvement. High resolution inspection data carried out by Drone, would enable planners to form accurate decisions on high priority areas for maintenance, thereby reducing potential downtime of network assets.

Once the data has been captured, and the AI trained, condition based risk monitoring scores and out- puts can be created, identifying potential risks, and hazards to the network, and allowing maintenance planners to better create maintenance schedules.

CASE STUDY

Smart Data worked with a leading Telco provider in Australia to roll out an inspection program over their remote, distributed tower assets.

The project realised a number of benefits particularly around safety, cost efficiences, access to data, and review and collaboration of that data across the organisation with multiple stakeholders able to access and review in a collaborative cloud platform.

PROCESS	APPLICATION	BENEFITS
Asset inventory	• Digital cataloging • Quantity, location and quality of inventoried as- sets	• Inventory manage- ment through shared access • Data available to facilitiate effective decision making • Digital portfolio to make decisions
Asset Manage- ment	• Condition assessment of infrastructure with drones	• Workers safety • Decreased inspection costs and greener process • Interactive 3D Models (where re- quired)
Defect Identifica- tion	• Automated Corrosion Identification	• Reduced manual review • Increased worker productivity on other tasks

OPPORTUNITIES – DIGITAL TRANSFORMATION AND ASSET DIGITISATION

Using a combination of technoligies, towers can now be accurately scanned to extremely high levels of accuracy, and digital twins created of an asset, moving Telco providers from paper diagrams and draw- ings of their structures, to actual, digital models available in Smartdata where assets can be tagged and highly accurate models shared within the organisation, or with third parties either who are completing work on the asset. These scans can also be used for Inventory purposes.

Highly accurate models, can also be used to calculate existing space on the tower, allowing identifica- tion of revenue generating opportunities where addional antennas. These interactive models and point clouds can be navigated using our Smartdata software platform, which is also used for visualisation of inspection programs.

OTHER OPPORTUNITIES

Smartdata allows the viewing of the captured 3D model beside the BIM, or design model, to ensure installations have been conducted according to design. Telco providers are also able to then utilise these models to accurately to monitor change over time, as well as link assets back to a central asset management system.

Today – due to improvements in technology, this price is being driven down even further. Dual capture from an RGB camera and thermal camera allows defects to be identified, as shown in the image below. As each image is geo-located then individual faulty panels can easily be located, and replaced. Once an entire solar project has been scanned, it is possible to estimate efficiency losses due to faulty panels.

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Briefing & Opportunities for the Power Industry

Pixeld — Thu, 17 Feb 2022 01:07:18 +0000

SUMMARY

Following on from a training and development package prepared and delivered for a leading Power and utilities company, this document has been put together to help identify future opportunities for Remotely Piloted Aircraft (RPAS), in the Power and Utilities sector. We aim to identify other cases around the world where efficiences have been realised by util- ising RPAS technology to improve monitoring of networks.

The original trial for the Power and Utilities company was utilising RPAS technology to help with fault finding and troubleshooting of the current network when outages were no- tified. It is our understanding that this part of the trial hasn’t been as successful as hoped, due to a number of factors, but largely due to the fact that the Drone hasn’t been able to be deployed as regularly as hoped, and that faults haven’t been as prevalent as usual during this part of the season.

OPPORTUNITIES

The opportunties for Drones in the power in- dustry are plentiful. There are situations where drones will allow data to be captured in a more efficient manner, saving time and resources that can be diverted elsewhere. This can be anything from asset inventory calculations, through to asset management and condition reporting, and vegetation management.

It is estimated that power and utilities sector losses related to network outages cost $169 billion globally.

Drones can be used to improve the reliability of supply through condition monitoring, and fault finding on areas where supply and faults may already be identified.

Leveraging additional technologies such as deep learning, and artificial intelligence will enable further savings, cost reductions and efficiences to be realised, thereby enabling better outcomes for both customers, and P&U companies themselves.

REGULATIONS

One of the current challenges preventing Drones from being utilised more regularly in power applications, is the requirement in Aus- tralia to keep the RPA within line of sight of the person conducting the operations. Typically, this limits the radius of operations to 300-400 metres, depending on the person’s eyesight, and size of the RPA itself.

Future iterations of the regulations, namely the manual of standards will allow drones to be operated under what’s called extended visual line of sight (EVLOS)

The updated extended visual line of sight reg-ulations allow for the RPA to be operated up to 1500 metres away from the relevant observer. There are 2 classes of EVLOS operations pro-vided for under the Manual of Standards.

There are additional opportunity sets to be re- alised in power line corridor surveys. Tradition- ally this has been completed by manned fixed wing aircraft and helicopters, combined with satellite imagery in some cases. These aircraft carry expensive LiDAR sensors, and cameras to map, and create a point cloud/mesh of the condition of the network, as well as identify vegetation encroachment areas which could cause network outages.

Drones have the ability to capture a higher quality dataset, generally with more informa-tion per image/scan than traditional methods. Valuable analytics can then be extracted from each asset, whether that be an individual pole, transformer, substation or otherwise.

To this point, the technology has far exceeded the regulations keeping up with the capabilities of the RPA’s. New regulatory processes mean that the potential for Drone technology for longer projects such as corridor mapping is now a real possibility in the power sector.

There are industry estimates that flying Drones BVLOS will cost between $200-$300 per mile, compared to helicopter flights which can average $1200-$1600 per mile, a significant reduction in costs.

OPPORTUNITIES – NETWORK INSPECTION PROGRAMS

In the past, network mapping and inspection has been completed via helicopter flights to aggregate LiDAR and imagery data for further analysis. Integrated hardware sensors on the RPA itself
allow the capture of high resolution data such as RGB imagery, thermal imagery, and LiDAR data.Capture from multiple angles of a pole/asset allows the generation of 3D models, useful for mainte- nance planning, situational awareness, and fault finding. An example of a 3D model from a pole in WA is shown below.

Where power quality is suspected to be an issue, an inspection of all relevant assets (poles and towers, crossarms, hardware and fittings) on a pole can be conductedBy combining this data with thermal imagery defects can be identified. Thermal imagery will allow or- ganisations to determine if electrical componentary is operating within effective limits or not.

Thermography survey of line cables would allow potential hotspot identification as well as potentially show possible earths in SWER lines. Thermal technology can also be used on substations, allowing for predictive maintenance

Opportunities – Deep Learning And Artificial Intelligence Analytics

Where real cost savings and efficiencies are able to be realized in power networks, is using deep-learn- ing resources to identify and report on individual elements and record asset inventory. Deep learning is a subset of Artificial Intelligence.

Deep learning models perform well when the data used to train the model is of a high standard, and has been labeled correctly in the first instance. There are elements of deep learning models using re-en- forcement training among other things, but the first step to correctly deploying deep learning and data analytics is to ensure the data captured is of a sufficiently high quality. A poorly trained deep learning model will create additional challenges with false positives and incorrectly identified aspects requiring re-training.

By coupling automatic recognition of assets and inventory, as well as identification of defects on struc-tures, and potential vegetation encroachment many of the challenges associated with manual inspec-tions can be solved. Obviously large amounts of assets don’t lend themselves well to being reviewed by humans, when the datasets captured are quite large. Deep learning can be used to identify initial areas of interest, which can then be reviewed for accuracy.The overall benefits mean that speed and quality of data capture is improved, as well as a reporting and analytics improvement. High resolution inspection data carried out by Drone, would enable planners to form accurate decisions on high priority areas for maintenance, thereby reducing potential downtime of network assets.

LIDAR

LiDAR and photogrammetry both have their strengths and weaknesses – depending on the system to be used. LiDAR uses light puls-es emitted from a rotating sensor, and then measures the time taken for a return to reach the sensor. As the speed of light is a known figure, then the distance to an object can be calculated. From this, an XYZ co-ordinate can be applied to that point. The LiDAR sensor is capable of capturing nearly a million points per second. From this a detailed point cloud can be generated.

An example of a lidar captured point cloud is shown in the video below.

LiDAR for power assets can be useful for

• Heavily vegetated areas and identifying vegetation encroachment
• Powerline sag surveys, particularly during hot weather and peak loads
• Complex structures (such as substa-tions)
• Night time operations (as the LiDAR uses a laser scanner as opposed to a camera) then point clouds can be generated during night flights
• Operations which require faster process-ing and turnaround times than imagery. Standard rule for LiDAR processing is 3 times the flying time for data processing. For volumetrics, a point cloud can often be generated immediately after the flight has finished.
• Generally less flying required – a LiDAR overlap on a swath may be around 20% side lap, whereas in imagery 60-70% can be required
• LiDAR is beneficial for picking up thin wires/ structures such as power-lines

PHOTOGRAMMETRY

Photogrammetry is progressing at an ex- tremely rapid rate, allowing the creation of complex datasets in the form of 3D Models, Point clouds, Orthomosaics, DTM’s and DSM’s to be created from standard imagery, provid-ed enough overlap is captured. Photogram- metry does require large amounts of overlap between subsequent images, and longer pro-cessing times.

An example of a photogrammetry derived pipeline point cloud is shown in the video be- low.

The advantages of photogrammetry are –

• Point clouds can be generated, however these point clouds are colourised with the pixel data captured by the camera. This is an advantage over the height returns or intensity returns shown by a Lidar alone.
• Raw imagery can also be used for condi-tion reporting
• Colourised products such as Orthomosia-ics and 3D Meshes can be generated
• Can also be used for measurement of vol-umetrics and other data either using the point cloud, DTM, or DSM
• Easier to identify objects in the point cloud due to the colourization

CASE STUDY

PwC worked with California’s leading power and utilities company, deploying a combination of remotely piloted aircraft, machine learning, and advanced image data analytics. Smart Data has entered into a Joint Business Relationship with PwC in Australia to co-market our services.

The idea behind the project was to reduce the number of outages and operational issues related to vegetation management and encroachment and also increase the effectiveness of their asset man-agement.

Whilst no figures in terms of efficiencies are available the following tangible benefits were realised.

PROCESS	APPLICATION	BENEFITS
Automated asset inventory	• Digital cataloging • Quantity, location and quality of inventoried assets	• Inventory manage- ment through shared access • Data available to facilitiate effective decision making • Digital portfolio to make crisis de- cisions
Asset manage- ment	• Condition assessment of power and utility infra-structure with drones	• Workers safety • Decreased inspection costs and greener process • Interactive 3D Models
Vegetation manage- ment	• Using LiDAR technology and photo- grammetry for vegetation mapping and analysis	• Strategic and data-driven level of work forecasting • Tactical vegetation work planning

OPPORTUNITIES – LONG RANGE INSPECTIONS USING RPAS

Another opportunity for cost savings and efficiencies will be utilising long range RPAS to conduct net-work inspection and monitoring programs, over larger distances (say 100km sections). The technology is starting to allow for these inspections to become business as usual, and the regulations will start to catch up over the next 2-5 years. Power companies who anticipate this early, will be in a good position to adopt the technology faster, than those who don’t.

CASA is adopting a SORA (Specific Operations Risk Assessment) process to reviewing BVLOS applica-tions. This is less cumbersome than how CASA previously reviewed these projects, and takes a quanti-tative approach to risk assessment.

In time, the current network monitoring that takes place utilsing helicopters, could be completed using long range, long endurance drones. This would mean less risk for aircrew operating at low level, in less than favourable conditions, and the data deliverables are likely to be of a higher quality.

A sensor suite comprised of RGB cameras, LiDAR, thermal imaging cameras, and potentially multi-spec-tral or hyperspectral cameras could be carried to capture an array of data for analysis. Capturing all of this data and then merging it, would allow extremely valuable insights to be generated.

This will be an area where a large cost benefit saving can be realized. Manned aircraft are expensive to operate from a fuel, insurance and mobilization point of view, and have aircrew costs. Remotely piloted aircraft operate at approximately 1/10th of the fuel cost, and have reduced maintenance and other operational costs as well.

This will be a natural progression from helicopters and fixed wing aircraft to Remotely Piloted Aircraft, especially as the regulations to conduct these sort of operations become more flexible. Power com-panies that are investigating, and trialling these technologies now will be well placed into the future to realize the benefits of automated inspections from Remotely Piloted Aircraft.

Long range RPAS operations would be particularly suited to remote area inspections and data collec- tion, such as in Western Australia, and North West Queensland, where the network may not be inspect- ed as regularly as metropolitan areas.

OTHER OPPORTUNITIES

Whilst not in the traditional energy generation and distribution portfolio, Solar farms are one area where drones are showing major efficiencies from data capture through to image analytics. Traditionally, inspections of solar farms were done by field workers, utilizing handheld thermal imaging cameras to detect anomalies.

By utilsing remotely piloted aircraft, there is a large efficiency gain to be made in the time to conduct the inspection, as well as a cost reduc-tion.

Traditionally, cost estimates were made at around $1 per panel to con-duct the inspections. These were generally completed post construc-tion and completion of the solar project, and then again at intervals, generally yearly or every two years.

A case study showing the details of the project above is available upon request.

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Using Artificial Intelligence To Detect Corrosion

Pixeld — Wed, 26 Jan 2022 21:43:21 +0000

Perhaps the main source of damage to human-made structures worldwide is corrosion. More often than not, corrosion takes the form of rust, the ochre coloured layer we all know and that develops over time on metal surfaces.

Given enough time, corrosion will eventually extend beyond the surface of the metal and lead to potentially catastrophic failure. Inspecting structures for corrosion is one of the main tasks in asset inspection worldwide, consuming extraordinary amounts of time and monetary resources. But what if there was a way to automate this procedure and to make it more reliable?

: Side by side comparison: National Drones ‘rusty’ algorithm identifying corrosion

Utilising Drone Data

One way to speed-up the process of asset inspection is to fly drones equipped with cameras around a particular structure, something we are experts at doing at National Drones. Moreover, out proprietary platform Smartdata allows for seamless navigation of the collected data, as well as the integration of different data sources and formats, such as 3D models and point clouds. However, going through potentially thousands of images or navigating a 3D model in search of structural damage is still a time-consuming process. Ideally, potential areas of damage would be highlighted before a human can review them. Not only does this make the inspection process quicker but more objective and less prone to human error.

: Identifying rust on top of a power pole in Smartdata utilising Artificial Intelligence

Artificial Intelligence At The Core

At National Drones our vision is to have Artificial Intelligence (AI) at the core of what we do. For this reason, we are introducing automated corrosion detection into Smartdata, powered by Deep Learning. The process for the user could not be simpler: upload images of an asset to Smartdata in the same way as you would for other data outputs, choose whether you want AI to run through the images and highlight areas affected by corrosion. You can then choose to visualise the corroded areas at the touch of a button, greatly speeding up the process of inspecting assets that are impacted.

In time, we plan to add many other types of automated detection of structural damage to Smartdata, with the ultimate goal of producing automated reports and even predicting whether structures need repairs or if they are in danger of failing.

The Use Of Deep Learning

Using Deep Learning for our corrosion has two main advantages: scalability and transferability.

Scalability

Scalability means that we can constantly add more data to the AI algorithm, and it will always improve its performance; so the more data we feed it the better the performance in detecting corrosion.

Transferability

Transferability means that we can use the AI to detect corrosion in any type of asset, irrespective of what type of data it was trained on. So, for instance, if we train the AI to detect corrosion in metal towers, it will do a good job of detecting it in bridges, without needing to change anything in algorithm. However, if we have image data for bridges, we can feed it to the model and augment it in order to make it better at detecting corrosion in this new type of asset.

Want To See The Benefits For Your Industry?

At National Drones we are very proud of our new corrosion detection algorithm, which we affectionately call “rusty”. We hope to see rusty develop into the best tool in the industry to detect corrosion. It already produced amazing results, even in its infancy. Many industries are set to transform rapidly utilising AI. With a constant need to innovate, drive efficiencies, and reduce costs we believe the application of aerial data and AI will be critical for businesses and change is already occurring.

Interested to learn more? Get in touch to discuss how we can help your business innovate.

The post Using Artificial Intelligence To Detect Corrosion appeared first on SmartData.

SmartData

Briefing & Opportunities for the Telecommunications Industry

SUMMARY

OPPORTUNITIES

REGULATIONS

OPPORTUNITIES – TOWER INSPECTION PROGRAMS

OPPORTUNITIES – DEEP LEARNING AND ARTIFICIAL INTELLIGENCE ANALYTICS

CASE STUDY

OPPORTUNITIES – DIGITAL TRANSFORMATION AND ASSET DIGITISATION

OTHER OPPORTUNITIES

What Were Some Of The Unique Challenges, Solutions And Results?

Briefing & Opportunities for the Power Industry

SUMMARY

OPPORTUNITIES

REGULATIONS

OPPORTUNITIES – NETWORK INSPECTION PROGRAMS

Opportunities – Deep Learning And Artificial Intelligence Analytics

LIDAR

PHOTOGRAMMETRY

CASE STUDY

OPPORTUNITIES – LONG RANGE INSPECTIONS USING RPAS

OTHER OPPORTUNITIES

What Were Some Of The Unique Challenges, Solutions And Results?

Using Artificial Intelligence To Detect Corrosion

Utilising Drone Data

Artificial Intelligence At The Core

The Use Of Deep Learning

Want To See The Benefits For Your Industry?