AR Fault Visualization for Proactive Building Equipment Maintenance
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Solution Overview
Problem
Current building maintenance practices are inefficient due to reactive maintenance approaches, reliance on inexperienced personnel, and lack of proactive fault detection, leading to equipment downtime and increased labor and scheduling challenges.
Innovation Solution
A system that uses a server to associate building and equipment identifiers with sensor data, identifies faults in real-time, and generates augmented reality (AR) content for maintenance staff, providing location-specific and actionable insights for equipment maintenance and repair.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If reactive maintenance is performed based on tenant complaints, then equipment is maintained when problems arise, but equipment downtime increases and maintenance efficiency decreases
Solution Approach 1:
The system performs preliminary fault detection by continuously monitoring equipment parameters through sensors and comparing them against baseline values. Faults are identified before they manifest as operational failures, enabling preventive maintenance actions to be taken in advance, thus eliminating equipment downtime associated with reactive repairs.
Solution Approach 2:
The system establishes a feedback loop where sensor data from equipment is continuously collected, analyzed, and used to update maintenance decisions. Real-time monitoring feedback enables the system to detect deviations from normal operation and trigger maintenance workflows before complete failure occurs, improving equipment availability.
2Productivity
If inexperienced maintenance personnel are used, then labor costs are reduced, but maintenance quality and efficiency decrease
Solution Approach 1:
The system introduces an intermediary layer of automated fault detection and diagnosis that bridges the gap between inexperienced technicians and complex maintenance tasks. The system processes sensor data, identifies faults, and provides structured guidance, allowing junior technicians to perform maintenance with expert-level accuracy without requiring extensive training.
Solution Approach 2:
The maintenance system performs self-diagnosis by automatically analyzing sensor data and identifying equipment faults without human intervention. This eliminates the need for technicians to possess deep diagnostic expertise, as the system independently determines the nature and location of problems, enabling less experienced personnel to effectively perform maintenance tasks.
3Productivity
If proactive fault detection is implemented, then maintenance efficiency is improved, but system complexity and initial costs increase
Solution Approach 1:
The system employs universal sensor platforms and monitoring algorithms that can be applied across multiple equipment types and building scenarios. By using multi-functional components that serve various maintenance needs, the system reduces overall complexity compared to specialized dedicated systems for each equipment type, making proactive maintenance scalable and cost-effective.
Data Source
AI summary
This technology enables a receipt of a plurality of readings from a sensor monitoring a piece of equipment in a building. The receipt enables an identification of a present fault or a projected fault in the piece of equipment. The identification enables a generation of an augmented reality content related to the present fault or the projected fault. The augmented reality content is sent to a mobile device when the mobile device is in proximity of the piece of equipment.