Predictive Maintenance with Vision AI
Blog post from Roboflow
The tutorial explores the implementation of predictive maintenance using vision AI by integrating RF-DETR and Gemini 2.5 Pro within Roboflow to detect equipment defects before failure occurs. This approach leverages cameras and trained computer vision models to identify early-warning signs of equipment issues such as surface defects and corrosion, preventing costly unplanned downtime. The process involves training an RF-DETR model with annotated bearing images to classify conditions like good, scratch, rust, or grease, and deploying it in Roboflow Workflows to automate visual inspections. The model's predictions are analyzed by Gemini 2.5 Pro, which provides maintenance insights, including the likely causes and severity of detected conditions, and recommends actions. This integration of computer vision with maintenance reasoning enables efficient detection and assessment of equipment health, offering a scalable solution that can be extended to various industrial applications beyond bearings.
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