Predictive Maintenance

In both solutions, maintenance costs are very high, and especially due to unplanned downtime, the factory incurs additional costs not only for the cost of the part but also from the production.

Problem

In most factories, 70% of the maintenance plans are still reactively operated and parts are replaced/repaired when they are failed, or over-maintenance is practiced with continuous and excessive maintenance even if the part performs as intended, aimed to prevent delays or stoppages in the factory.

In both solutions, maintenance costs are very high, and especially due to unplanned downtime, the factory suffers additional costs not only for the cost of the part but also due to the production loss. The optimum solution to this problem is provided by predictive maintenance, which introduces estimation of possible failure, by processing data from sensors and planning of maintenance works.

Predictive maintenance, ensures up to 70% reduction in unplanned downtime, increases equipment availability around 10-20% and curbs total costs by an average of 10-30%.

Solution

With our predictive maintenance solution, sensor data received through IoT platforms are analyzed using artificial intelligence algorithms together with stoppage and failure information in the operational systems, generating equipment/part breakdown estimates, anomaly detections and failure probability calculations. This optimizes maintenance costs by allowing early precautions to be taken and correct planning for predicted failures.

Actions can also be taken for spontaneous anomalies by monitoring all flows over dashboards and triggering the necessary alarms. Continuously learning artificial intelligence algorithms self-improve and increase the accuracy of analysis for the detection of anomalies and new failures that may occur after the system is put back into operation.

Benefits

Lower Maintenance Costs

Elimination of unplanned downtime based on the failure predictions from the system reduces production costs.

Increased Equipment Operability

The application increases the average operating time (not in maintenance) of all equipment.

Average Repair Time

The total maintenance time is reduced with estimation-based maintenance instead of continuous periodic maintenance.

Maintenance Schedule by Predicting Failures

The time of malfunction is predicted for each analyzed part and a maintenance schedule is created accordingly.

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