EAM (Enterprise Asset Management) is a solution specifically developed to optimize the performance of production equipment, predict failures in advance, and ensure scheduled maintenance. EAM combines critical functions such as data analysis, failure prediction, inventory management, and preventive maintenance. It enhances production continuity, ensures work safety, and minimizes costly downtimes.
For EAM, it is essential to continuously collect data from production equipment. Using sensors and monitoring devices, data is gathered by measuring parameters such as equipment operational status, vibration, temperature, pressure, and flow rate.
The collected data is analyzed and processed to detect signs of malfunction or problematic conditions. Using techniques such as statistical methods, data mining, and machine learning, predictions about the equipment's future performance or the likelihood of a breakdown are made.
Planning Based on the results of data analysis, predictions are made regarding when the equipment might malfunction or cause issues. These predictions are typically expressed as a “fault probability value” or an “estimated time to failure.
Based on these fault predictions, scheduled maintenance is planned. Maintenance actions are carried out just in time for critical equipment based on predetermined fault threshold values. In this manner, regular maintenance is ensured for the equipment before it leads to a malfunction.
Thanks to EAM's fault prediction and scheduled maintenance dating, it allows for the effective management of spare part inventory. This aids in optimizing supply processes and keeping stock levels under control.
The primary purpose of EAM is to implement scheduled maintenance, take preventive measures, and minimize unexpected breakdowns. In this manner, production continuity is enhanced, occupational safety is ensured, and costly downtime can be prevented.
Thanks to the continuous monitoring of production processes in a digital environment, it makes performance and maintenance activities numerically predictable.
Thanks to the timely maintenance of production equipment, it prevents a decline in efficiency. It contributes to the improvement of production processes.
Thanks to the recorded maintenance and part wear durations, it allows for fault prediction based on production processes and targets.
Based on fault prediction data, it anticipates parts that are worn out or need replacement in advance. This prevents workplace accidents or performance degradation.
It eliminates unforeseen malfunctions that would halt production. By optimizing costly downtime, it ensures the continuity and efficiency of production.
By scheduling timely maintenance and repair for production equipment, it ensures that their performance remains at a high level.
