For large-scale lighting installations in airports, stadiums, corporate campuses, and cities, unplanned fixture failure is a major operational headache. AI-driven predictive maintenance is becoming the solution. By continuously analyzing data streams from connected luminaires—driver temperature, output current, voltage fluctuations, and hours of operation—machine learning algorithms can identify patterns that precede a failure. The system can then generate a work order to replace a driver or clean a fixture’s heatsink weeks before an actual outage occurs. This shifts maintenance from a reactive, costly model to a proactive, scheduled one. It maximizes system uptime, ensures consistent light levels, optimizes maintenance crew schedules, and protects the long-term investment by addressing issues before they cause damage to other components.
