The energy sector is under pressure to manage assets efficiently and cost effectively. To achieve these mandates, inspection teams are using new technologies to drive more value from their data and optimize their maintenance strategies.
We recently sat down with Harold Queen, President & CEO of Queen Consulting Services, to discuss the future of asset inspections. Harold provides testing and inspection SME and management advisory services to businesses seeking unprecedented growth. He has also served in senior leadership roles for organizations including Industrial Inspection & Analysis, Structural Integrity Associates, AEA Sonomatic, ABB Amdata, and the Tennessee Valley Authority.
What is driving companies in the energy sector to transform?
Industries, particularly those that perform inspections and testing, have been on a transformation path for a long time. However, the pace of transformation accelerated with the pandemic, as personnel needed to access information remotely.
Meanwhile, global economics are impacting the energy market. Since companies are spending less capital, they must manage their current assets more cost effectively.
Keeping assets running requires access to inspection and monitoring data. Inspection teams can aggregate this data into a platform that allows them to see what's happening in real time so they can spot problems that may lead to costly failures.
How do you think AI and machine learning can help the energy sector optimize inspections?
Companies in the energy sector have captured and stored vast quantities of data for decades. But they often don't know what to do with this data after collecting it. They face challenges when it comes to data interpretation and reliability.
Machine learning and other forms of artificial intelligence can help inspectors get a handle on all this data. Digital platforms that incorporate machine learning and AI allow teams to perform inspections faster and remotely. But, for me, the most significant benefit is driving more value from inspection data. These platforms provide real-time, verifiable data that teams can use to optimize their predictive or preventative maintenance programs.
For example, The Electric Power Research Institute (EPRI)1 is investigating how AI can assist transmission and distribution line operators. The Institute developed AI algorithms that quickly evaluate millions of inspection images, photographs, and videos. The algorithms can distinguish between functioning and non-functioning assets—helping operators spot problems that need their immediate attention.
The energy sector is also using autonomous drones to inspect solar farms. In the past, an individual would drive through a solar field and examine all the panels manually. Now, with solar farms getting larger, this method is no longer practical or cost-effective. Autonomous drones can now inspect these sites and document anomalies in a single flight. Then, AI algorithms can manipulate the data to provide inspectors with insights into each asset's condition. The AI algorithms can determine when areas deviate from the norms and flag hotspots, cracks, discoloration, and other issues.
What else do you see in the future of asset inspections?
We'll see more automated asset inspections using technologies such as AI.
The energy sector pushed back on automation until recently, when experienced workers started to retire en masse. These workers often aren't replaced. At the same time, the number of industrial assets and the data they produce is growing exponentially. Energy companies are seeking ways to inspect all these assets with less staff.
Technologies such as drones, AI, and automation can perform inspections that aren't safe for humans—while filling the gap caused by the skilled worker shortage.
How can companies in the energy sector get started with digital transformation?
You can achieve quick wins by shifting to digital data records. Storing your inspection data in a digital platform makes it easier for teams to collaborate and share information with clients.
Then, you'll want to set up triggers that alert your members when they need to review the data. That way, your maintenance team can take prompt action to prevent failures.
Taking these steps will prevent different teams from doing their own thing and circulating documents by email—losing vital information in the process. Putting all your inspection data in a single platform allows everyone to work from the same chalkboard and view real-time data. It also minimizes the potential for data misinterpretation.
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1 EPRI Journal: Can Artificial Intelligence Transform the Power System, 2019