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Case Study : Automated Asset Inspection using Drone-based Image Processing for Preventive Maintenance

Automated Asset Inspection using drones

Overview Renewable energy companies face challenges in performing periodic inspections of widely distributed or large-sized assets, which are expensive and time-consuming. An automated solution is required to perform inspections quickly, accurately, and cost-effectively. Problem Statement Periodic inspections of renewable energy assets are difficult, especially when the assets are spread across large areas. Manual inspections are expensive, time-consuming, and prone to human error. Companies require an automated solution that can perform inspections quickly and accurately, reducing the risk of asset failure and downtime. Solution Approach We propose an automated solution using drones and proprietary deep learning-based software. The drone captures multiple images of the assets, which are processed by the software to create a single view of the asset. The software then identifies any damage or potential issues, generating a detailed report on the location and severity of the damage. Benefits Our solution offers numerous benefits. By using drones, companies can inspect their assets quickly and cost-effectively, reducing the risk of asset failure and downtime. The deep learning-based software ensures accurate identification of damage, enabling proactive maintenance planning. The detailed report generated by the software allows companies to make informed decisions on repair and replacement, reducing maintenance costs and extending asset life. Conclusion Our solution has been successfully deployed in the renewable energy sector, where it has helped companies perform preventive maintenance and avoid costly asset failures. By using drone-based image processing and deep learning, companies can streamline their inspection processes, reducing costs and improving asset performance.

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