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Predictive Maintenance

Predictive Maintenance

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.

Features

Integration with IoT platforms

The application can be directly integrated with Platform360, as well as working with data from other IOT platforms when the data structure is provided.

Ability to Operate on Different Algorithms

With the different algorithms included in the application, it automatically selects and optimizes the algorithm that best suits the data set and problem.

Continuous Learning

Algorithms improve their models with each new data, and better detect future problems.

External Data Feeding

External information such as temperature and ambient conditions can be fed into the application, to be employed as parameters in predictions.

Dashboard Overview

Dashboard view monitors all assets and presents an overview of general status.

Alarms

The alarm mechanisms enable audible, visual (HMI) warning or SMS/e-mail notification to be provided in any area, when certain conditions occur.

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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