Predictive maintenance isa strategy that uses data collected from equipment in operation to anticipate potential failures and schedule maintenance before they occur. Unlike time-based preventive maintenance, it relies on real-time data, often gathered by sensors, to determine the actual condition of an asset. This approach minimizes costly unexpected downtime and optimizes maintenance schedules by performing work only when needed. How it works Data Collection:Sensors are used to continuously monitor equipment for performance indicators like vibration, temperature, pressure, and oil condition. Data Analysis:The collected data is transmitted to a system where it is analyzed using statistical methods and machine learning to identify patterns that indicate a potential failure. Failure Prediction:Once a pattern is recognized, the system can predict when a failure is likely to happen. Maintenance Scheduling:Maintenance is then scheduled for the most appropriate time, just before the predicted failure, ensuring that work is only done when necessary.
Pejabat Utama
Green Energy Automation Sdn Bhd 201201043627 (1028104-T)
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