Predictive maintenance is a technique to foresee equipment failures through data analysis and condition monitoring.
Predictive maintenance involves the use of real-time data and analytics to predict and prevent failures in industrial equipment, thereby optimizing operational efficiency and reducing downtime. By evaluating data from sensors installed in machinery, such as vibration or temperature metrics, predictive maintenance allows for targeted interventions before substantial failures occur, making it critical in high-precision industries like automotive manufacturing.
In a manufacturing plant, sensors are installed on conveyor motors to collect vibration and temperature data, which are processed using a predictive maintenance platform to anticipate bearing failures.
This approach is commonly utilized in automotive parts production lines to avoid unexpected production stoppages and maintain output quality.
An oil refinery employs vibration analysis on its pumps to predict cavitation issues, analyzing frequency patterns to schedule maintenance before operational thresholds are exceeded, thereby prolonging pump life.
Predictive maintenance is vital in the petrochemical industry to prevent catastrophic equipment failures and comply with safety regulations.