Artificial Intelligence sustains critical infrastructure during COVID-19

The adoption of Artificial Intelligence and Machine Learning technologies has never been more critical. Due to COVID-19, many organizations need to find a new way of working. Ensuring production rates are reliable, if not increased, while limiting the number of personnel - in some cases down to 50%.

Many asset heavy industries, such as water, transportation & energy are considered critical infrastructure.  Every effort needs to be made to maintain these.  A large number of these assets are ageing and typically require sizeable maintenance crews to triage faults and errors, in the hope of preventing shut-downs for maintenance, which halts production. 

Even newer infrastructure comes with its own issues, as the volume of data produced can send engineers and data scientists in circles, un-able to identify true causes of faults and failures.

Artificial intelligence and Machine Learning technologies are being applied to maintaining infrastructure. Through the use of predictive analytics, the technology maps the live and historic data of IoT sensors on critical assets. Identifying the slightest deviations, predicting failures before they happen and the true root cause of issues.

The insights that can be obtained through this advanced technology can equip businesses to plan ahead for critical maintenance, acting before a failure happens, and ensures the safety of their teams.  What typically took hundreds of man-hours by a team of data scientists can now be done remotely by AI models.  Teams can access their asset’s health remotely on any device, removing them from the business entirely, allowing for limited personnel on site.  

Trevor Bloch, CEO of VROC said “AI Predictive Analytics can be quickly mobilized to critical assets, providing companies with the necessary tools to maintain production levels, keeping their workforces in employment, whilst ensuring the safety of all during COVID-19’.

Critical Infrastructure Use Cases:

Oil and Gas Resources – International agreements requires all countries to hold 90 days of oil reserves. As an example, Australia only holds a little more than three weeks, making the continued production of oil essential.  It is not only in the businesses best interests but also the government’s to ensure production reliability.

Oil Rigs contain many critical assets, such as Water Pumps & Gas Lift Compressors. Many operators are reducing staff levels to 50% to provide single occupancy dwelling for all personnel on site, and therefore need to look to technology to assist them in maintaining production.

See recent case study of successful use of AI in Oil and Gas

Renewable Resources – Wind turbines whilst relatively new assets are often in remote locations, making breakdown-maintenance methods impractical. The vast amounts of data produced by these assets makes it difficult to process rapidly. AI Predictive Analytics provides advance notifications for maintenance, assisting in keeping critical infrastructure operational.

Power and Energy – Power and Energy operators need to ensure energy remains reliable and efficient. Operators need to closely monitor water quality, temperature, lubrication and vibrations to ensure assets don’t overheat, and run at their optimum. AI Predictive Analytics can provide advance warnings of pending faults, outages and other issues in the power grid.

Critical Manufacturing – AI and Machine Learning can assist in maintaining critical manufacturing equipment such as generators, compressors, and other key equipment without a redundancy that is critical to production.   Advanced warning of faults helps to reduce overall maintenance expense, as material and personnel can be arranged without the need for costly urgent call out fees and deliveries.

Water Treatment Plants – This vital infrastructure can be maintained and even optimized through AI and machine learning technology, which leverages IoT sensors. Ensuring reliability with reduced personnel. See case study of the successful use of AI on a water treatment plant.

Transportation systems – Maintaining fast and reliable transportation systems for freight is critical with current border closes. AI Predictive Maintenance can predict failures on critical transport systems including cargo vessels, freight trains and trucks, with up to 7 days’ notice so maintenance tasks can be schedule with minimal disruption to operations. 

Mining – A reliable exportation from the mining sector forms a critical component of the world’s economy. Therefore despite COVID-19, production at mining sites needs to continue, with reduced workforces and strict social distancing practices.  AI and Machine learning technologies can easily be applied to critical mining assets to prevent shutdowns caused by unforeseen faults. The technology can be used to predict issues before they arise, allowing time for maintenance to ensure continued and optimized production.