PREDICTIVE MAINTENANCE OF EQUIPMENT (VIBRODIAGNOSIS)

Key laboratories carrying out the project are

– Educational and scientific laboratory «Artificial intelligence and cybersecurity»

Project description:

The project provides for the creation of an intelligent system for monitoring the technical condition of industrial equipment based on vibration diagnostics, machine learning and predictive analytics technologies.

Traditional maintenance methods are based either on routine repairs at fixed intervals or on the elimination of failures that have already occurred. Both approaches lead to significant economic losses associated with emergency equipment downtime and inefficient use of repair resources.

The proposed system provides continuous monitoring of the condition of equipment, automatic detection of defects and prediction of the residual life of main components and assemblies. During operation, a digital equipment status profile is formed, allowing you to plan repairs based on the actual technical condition.

The project is of particular value for the mining, metallurgy, energy and continuous production cycle enterprises.

Key technology solutions:

• Vibration diagnostics of equipment;

• Automatic detection of defects;

• Residual resource forecasting;

• Data mining;

• Digital models of technical condition;

• Support systems for repair services;

• Situational equipment monitoring centers.

Practical impact and results:

The transition to predictive maintenance can significantly reduce emergency downtime, reduce repair costs and extend the service life of equipment at the enterprise.

For large industrial enterprises, the economic impact can be millions of dollars annually by preventing unscheduled production shutdowns.