IMPROVING THE RELIABILITY OF ROBOTIC MANUFACTURING SYSTEMS BASED ON FAULT DIAGNOSIS, PREDICTIVE MAINTENANCE, AND REAL-TIME MONITORING

Abstract

The paper considers current approaches to improving the reliability of robotic and automated manufacturing systems based on fault diagnosis, predictive maintenance, real-time condition monitoring, and supporting automated infrastructure. The relevance of the study is determined by the increasing structural and functional complexity of robotic complexes, in which local defects of drives, reducers, bearings, sensors, or auxiliary subsystems may cause unplanned downtime and loss of process stability. The paper generalizes diagnostic, prognostic, and monitoring approaches and proposes a generalized structure of a reliability assurance system for a robotic complex that integrates physical subsystems, data acquisition, local diagnostic processing, IIoT-based data exchange, technical state analytics, and service decision support.