The technique based on data collected by sensors to anticipate errors.
Predictive maintenance is a technique that uses data analysis tools and procedures to detect anomalies in the operation and potential defects in equipment and processes, allowing them to be resolved before failures occur.
Just as predictive analytics can anticipate market movements or fluctuations in energy demand, predictive maintenance uses data analysis to predict system failures, becoming a fundamental part of industrial monitoring.
To monitor the condition of equipment and alert technicians about potential future failures, predictive maintenance consists of three main components.
Predictive maintenance differs from preventive and corrective approaches. However, all of these practices can be used simultaneously in the industry. Below, we will review their differences:
Predictive maintenance allows the maintenance frequency to be as low as possible.
When maintenance is scheduled periodically (preventive), two situations may occur: it is either performed when it is not needed (too early or too late), resulting in avoidable costs, or it is not done frequently enough, increasing the risk of equipment failure.
Therefore, the goal of predictive maintenance is to optimize the use of maintenance resources.
Predictive maintenance helps with early fault detection, reducing machine downtime.
Predictive maintenance ensures that equipment is only taken offline just before an imminent failure, avoiding unnecessary repairs.
The efficient operation of equipment is maintained, improving overall performance.
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