By David Jenkins
Many IPWEA members will wear safety helmets and hi-vis clothing and inspect the condition of the assets they manage.
In many cases, it is a time-consuming exercise that requires a lengthy journey to and from the site and time spent noting a range of criteria, historically on a clipboard but more recently on a digital device.
Those familiar practices are being transformed today by ever-improving wireless connectivity and an expanding army of sensing devices that can collect data for real-time analysis, increasingly using the power of artificial intelligence and machine learning.
Best-practice asset management has always required the most accurate knowledge of the state of the assets, and today, our sector is undergoing something of a data revolution.
Sensors are transforming physical assets into data collection points, which, when coupled with AI, can forecast asset degradation and recommend optimal times for repair, maintenance and replacement.
In the UK, for example, the robot “dog” Spot created by Boston Dynamics is out in the field surveying sections of a motorway in Somerset.
At the site on the M5 motorway, Spot has been scanning and capturing data on embankment sections using cameras and a lidar tracker.
Robotic inspection is also the rationale behind creating a new hub at the University of Sydney, launched to develop the use of robots and autonomous systems in collecting information on the state and performance of physical assets.
The aim of the ARC Australian Robotic Inspection and Asset Management Hub (ARIAM) is to equip robots to autonomously collect data, which can be used to create digital twins.
ARIAM’s work will also reduce the need for people to enter dangerous or hazardous locations to maintain assets such as tunnels and underwater infrastructure.
One of the companies at ARIAM is Nexxis, which is developing a spider-like robot with magnetic feet capable of crawling around metal structures and inspecting them for damage, going to areas that are not safe for human inspectors.
In addition to remote inspection, organisations are harnessing data flow to create digital twins that replicate their assets virtually, giving a more comprehensive view of the condition of a network of assets under management.
There are exciting developments in the world of digital twins, which will move beyond closed-loop systems to more immersive virtual environments that can simulate the impact of potential changes in the physical environment.
This will open up the potential to improve strategic decision-making, allow for automatic maintenance, and simulate real-world effects, such as stress testing resilience in cases of extreme weather events.
The successful implementation of data strategies is now a key priority for asset management organisations, and the new generation of tools combined with AI is a major opportunity to improve performance and efficiency.
The benefits can be experienced at all stages of asset management, from acquisition and commission to operations, maintenance and disposal.
Data can help standardise and automate design processes, freeing engineers to focus on value-added tasks such as performance optimisation and problem-solving.
Data can be harnessed for better communication, and its use in visualising concepts can help articulate ideas to stakeholders and drive engagement and buy-in.
It is in operations and maintenance, however, that the major benefits can be delivered.
More ingenious tooling can create better links between operations and asset managers and drive better collaboration between teams working on a shared and single source of truth, which can also automate workflows and document control.
However, this requires a mindset shift across organisations to integrate data collection and analysis into their operating models.
Rigorous work needs to go into the IT infrastructure and the digital backbone to optimise data collection, aggregation, and analysis, and insights must be shared to be clear and actionable.
Engineers will need to trust these systems completely and work with technology colleagues on their design if they are to confidently leave their hard hats and hi-vis in their lockers and rely on data and AI for their decision-making.
