Ahead of Service Works Global’s November 13 webinar, International Managing Director Samantha Fuller sat down with CCPPP to preview the exciting ways AI (artificial intelligence), IoT (internet of things) and BIM (building information modelling) can transform P3 operations. This interview has been edited for clarity and length.
What are some specific pain points that O&M organizations operating in Canada’s infrastructure sector must manage, that AI and the Internet of Things can help with?
Samatha Fuller: The complex nature of managing infrastructure projects, over long life cycles and involving many different stakeholders, means there are lots of moving parts. As any O&M operator will testify, managing this complexity can often turn into a frustrating game of “whack-a-mole.”
The fundamental pain point is a lack of meaningful, actionable data. Intelligent technologies such as Internet of Things sensors and AI-powered analytics platforms add visibility, accountability and a baseline for continuous improvement.
Sensor data can allow operators to monitor and measure asset performance, enabling them to identify trends and anomalies. These insights help improve the accuracy and efficiency of asset management. Understanding an asset's condition and when it’s likely to fail or even falter, for example, leads to more effective planned preventative maintenance, where a schedule can be developed that results in less under- or over-servicing, minimal operational downtime, and savings on costs associated with engineers’ travelling to site to fix things. Canada is vast – you better make sure that an engineer’s visit really counts.
Meanwhile, AI can crunch data and make calculations at a level impossible for humans or legacy IT systems to replicate. The technology can find patterns in the real-time data that don’t just highlight when an asset is close to failure but also the most ideal maintenance schedule based on multiple performance indicators such as vibration, temperature and pressure. This shifts maintenance from a preventative discipline to a predictive one.
Another common pain point for the O&M operators of infrastructure projects is managing payments. Payment mechanism software helps provide reports and trend analysis of services failures, deductions and rolling threshold values. Integrating sensor data and AI with paymech software imbues a level of transparency that helps avoid deductions while ensuring fairness for all stakeholders.
Finally, it’s impossible to ignore the enormous pressures now associated with net zero, especially in public infrastructure projects. The same combination of sensor data and AI allows operators to monitor their carbon emissions, measure to make improvements, and keep a record for all regulatory requirements.
Can you provide an example of how leveraging this technology has impacted operations of a major infrastructure project?
There is a great example in the Nordics. Jernhusen owns and manages several railway stations and properties across Sweden’s rail network. The company’s mission is to connect different modes of transportation and help create efficient transfer points for passengers. These aims tie in with Sweden’s Net Zero 2045 target, which the country is attempting to reach by encouraging more people to use public transit and decrease car use overall.
Station management is a challenge at the best of times, especially during winter. Wet and icy conditions can present a health and safety hazard and play havoc with key assets such as lifts and escalators. Something Canadians know all too well.
Our IoT platform and the integration of sensors has enabled Jernhusen to implement proactive, AI-driven predictive maintenance on assets such as escalators, which can make accurate weather predictions and helps avoid maintenance work during peak commuter times.
For example, Sensors have drastically reduced breakdowns through accurate sensor data from sources such as escalator run time, humidity, footfall and weather conditions. Using our asset management tool, Jernhusen’s FM team can monitor the weather forecast for the days ahead and when roads are likely to be gritted as a result. Grit is easily trampled into a station and is a common cause of escalator malfunctions as it gets clogged up in the belt. From here, our system sends alerts to FM teams of when to expect grit, helping them to prepare in advance. Escalator malfunctions have dramatically decreased since adopting this AI functionality.
How can O&M organizations operating in Canada’s infrastructure sector determine how — and when — to implement technology such as AI or IoT with minimal disruption to existing performance measuring methods, data collection, etc.?
The key is to start small by making sure all the right data is being collected and sensors are in the right places, and ensure you get feedback before scaling up or rolling these out elsewhere.
One solution is to start by focusing on just a couple of systems. Another is to set up a pilot project or site. This will allow operators to monitor the data collection and flow process, adjust where necessary and refine settings.
Operators must first ensure that the building’s network infrastructure and power supply can support sensors. Then, it’s all about integrating these with existing building management system(s). When asset sensors are connected to a BMS, their performance can be compared to pre-established limits, allowing you to determine whether it is working correctly or not. Readings outside of the limits will automatically alert the integrated CAFM software, creating a job and assigning an operative.