Integrated Diagnostic System and Multi-level Reliability Modeling for Predictive Maintenance of Transport Induction Motors
Year: 2025
Type: article
Abstract: The aim of this study is to develop the architecture of a diagnostic system and a multi-level reliability model aimed at improving the cost-efficiency and operational reliability of induction traction motors. To achieve this goal, a comprehensive development process was carried out: based on failure statistics, the main defects and types of damage to critical motor components were identified for monitoring and timely maintenance; the architecture of an integrated diagnostic system for assessing the technical condition of motor components (stator, rotor, bearings) was designed; and a reliability model was constructed, reflecting eight operational states of the motorfrom full functionality to catastrophic failure. The reliability assessment is based on the Kolmogorov-Chapmansystem of equations, which describes probabilistic transitions between operational states and allows accurate prediction of failure-free runtime and optimal maintenance intervals based on current parameters. The most important results include the formalization of the relationship between diagnostic signals and reliability states and the construction of the reliability function for dynamic maintenance scheduling. The significance of the results lies in the integration of diagnostics with probabilistic reliability modeling, which ensures higher failure prediction accuracy, reduced unplanned downtime, and improved operational readiness. A key contribution of the study is the practical implementation of the eight-state model, enabling dynamic maintenance planning and adaptation of service strategies to the real-time condition of equipment. The proposed strategy lays the foundation for intelligent control of traction drives and the implementation of predictive maintenance systems, which is especially relevant forrail transport and other industries using electric drives.
Language: English
Subfield: Safety, Risk, Reliability and Quality
Field: Engineering
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4416465629