Condition Monitoring (CM): the process of monitoring specific parameters (like vibration, temperature, lubricant condition, etc.) of critical equipment or systems to detect any significant change which might indicate a developing fault.
It’s a “snapshot” of the current condition at a particular time.
Predictive Maintenance (PdM): involves using data-driven, pro-active maintenance methodologies to predict when equipment failure might occur, so maintenance can be performed just in time to avoid unplanned downtime.
Predictive Maintenance often uses the data obtained from Condition Monitoring, but applies analytics, algorithms, and sometimes machine learning to predict future failures.
Purpose:
CM: Detect and monitor changes in machine conditions to highlight potential problems.
PdM: Forecast when a machine will fail or when a maintenance task should be performed to prevent an unplanned outage.
Method:
CM: Regular or continuous measurement of equipment parameters and comparison against predefined standards or baselines.
PdM: Analysis of data trends and patterns (often with the help of advanced software tools) to predict the future condition of the equipment and schedule maintenance accordingly.
Frequency:
CM: Monitoring can be continuous, daily, weekly, monthly, etc., depending on the criticality of the equipment.
PdM: The frequency is determined by data trends and the analytics’ outcomes, leading to predictions on when maintenance might be required next.
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