BACKGROUND
Effective facility management is a crucial component for building functionality and safety, and despite current upkeep efforts, many facilities managers find themselves stuck with Unplanned Maintenance (UPM). Unplanned Maintenance significantly disrupts building functionality despite extensive Planned Preventative Maintenance (PPM) efforts leading to downstream ripple effects that can disrupt the entire building systems due to misunderstood component dependencies. Therefore, facility asset management remains a significant challenge across large and complex infrastructures when identifying individual components quickly and understanding their maintenance implications can save considerable time and cost.
SUMMARY OF TECHNOLOGY
Oklahoma State Researchers have developed an innovative solution to facilities management, that harbors AI-technology to “see” the problem and provide solutions. EyeFM, is an AI-driven mobile software program designed to revolutionize facility asset management through reverse image intelligence. EyeFM enables facility managers and technicians to rapidly identify HVAC and related equipment assets simply by capturing images with a mobile device. Utilizing state-of-the-art deep-learning models trained on a comprehensive facility asset image dataset, EyeFM matches photographed assets to a detailed repository, delivering instant identification and maintenance history access. The system further incorporates advanced casual analytics to reveal how the failure or maintenance status of one component impacts interconnected parts within the facility ecosystem, supporting proactive and efficient maintenance planning.
POTENTIAL AREAS OF APPLICATION
MAIN ADVANTAGES
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