A large copper mine processing plant faced repeated high-cost shutdowns caused by unexpected failures in crushers, conveyor systems, and ball mill foundations. Traditional maintenance methods based on periodic inspection were unable to detect early-stage fatigue, bearing wear, and structural stress accumulation. OctaSense deployed a full-spectrum sensor network combining vibration sensors, strain gauges, thermal cameras, displacement probes, and acoustic emission monitoring. An AI-based predictive maintenance system continuously analyzed equipment health, detecting anomalies in real time and estimating remaining useful life of critical components. This enabled maintenance teams to shift from reactive repairs to scheduled interventions during planned shutdowns, significantly improving operational reliability and reducing downtime.