Analytical Research Regarding the Implementation of a Prescriptive Maintenance System Applied to IT&C Equipment

In the era of digitalization, prescriptive maintenance and equipment condition monitoring are essential for ensuring the continuity of operations in various industries. The proposed software solution provides an integrated solution for monitoring and maintaining equipment, facilitating the collection, processing and interpretation of data related to their performance. Prioritization of prescriptive maintenance recommendations for alerts within the application is based on a set of well-defined criteria that integrate both the analysis of physical sensor data and computer logs. This is achieved through a hierarchical classification mechanism, combining predefined thresholds, prescriptive assessments and severity levels. The proposed project is a prescriptive equipment monitoring and maintenance application focused on the collection and analysis of sensor data to identify and prevent potential failures. The application structure integrates components such as data processing, machine learning, databases and recommendation generation, each of which has a specific role in the project workflow. The integration of sensor data and logs enhances robustness, scalability and efficiency, ensuring both high performance and adaptability to the varying needs of users. The proposed system processes and analyzes large data volumes. It combines threshold-based logic with machine learning to enhance adaptability.