AI-driven solutions for remote and sustainable monitoring of industrial assets, energy infrastructures, and emissions
Overview
We supported a global leader in Oil & Gas technology manufacturing to develop a solution based on drones, sensors, and machine learning algorithms to monitor assets and detect methane leaks. The project developed an automated model for image acquisition and analysis, improving safety, timing, and monitoring and maintenance activities.
Background
The client is a global leader in the production of technologies for the Oil & Gas sector, with numerous production sites and operational infrastructures worldwide, including several industrial plants in Italy.
Managing and monitoring these assets requires periodic inspection activities to ensure high operational standards, prevent structural issues, and reduce the environmental impact of industrial operations.
Challenge
The client needed to make inspection activities on its industrial assets faster, more accurate, and safer. Traditional monitoring procedures, based on manual inspections, require significant time, specialized personnel, and direct access to structures that are often difficult to reach.
In this context, the company was looking for a solution capable of reducing inspection time, improving accuracy in the identification of potential issues such as methane leaks or structural anomalies, and limiting operational risks for personnel.
The challenge, therefore, was to develop an innovative and scalable approach to optimize industrial asset monitoring processes, while ensuring greater reliability of results and more effective support for maintenance and environmental control activities.
Approach
The project began with the definition of a standardized inspection framework. This phase included the definition of operating protocols, image acquisition methodologies, instrumentation and sensors (infrared and laser cameras) for emissions detection, and reporting standards applicable across the different sites.
Subsequently, in collaboration with a technology partner, survey campaigns were carried out using drones and dedicated aerial technologies capable of capturing high‑resolution images of the industrial assets and infrastructures to be monitored.
The collected images were then processed through data workflows and custom machine learning models capable of automatically identifying the elements required by the client and detecting potential anomalies or issues. This phase made it possible to transform large volumes of visual data into structured and easily analyzable information.
Finally, we developed a data analysis and reporting system that centralizes the collected information to support inspection traceability, historical comparison, and maintenance planning. This approach enabled the creation of the foundations for a scalable digital asset management model applicable across multiple industrial sites.
Delivery
Results Achieved
The project demonstrated the effectiveness of AI‑Driven Drone Inspections in improving industrial asset monitoring and methane leak detection in the following use cases:
• Test with controlled methane leak inside a warehouse (simulated case)
• Tests on the client's industrial infrastructures (real case)
• Inspections of warehouse roofs at a production site (real case)
The tests confirmed the solution's ability to detect methane leaks with higher accuracy than previously used methodologies and to automate anomaly detection on assets.
+3k
high‑resolution images collected and analyzed
+250
structural anomalies automatically identified