By integrating data-driven approaches, MCS can help improve the efficiency, reliability, and cost-effectiveness of maintenance operations. We use: Condition-based monitoring: Sensors installed on critical systems (HVAC, plumbing, electrical, etc.) can detect early signs of wear or failure (e.g., temperature fluctuations, vibration patterns, or pressure changes) and predict when maintenance is needed.
Machine learning: Algorithms analyze historical data to predict the future likelihood of system failures, allowing maintenance teams to intervene before issues arise.
Asset life-cycle tracking: Predictive models help estimate the remaining useful life of equipment, optimizing replacement schedules and extending asset life.
