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A fuzzy logic-based multi-sensor diagnostic system for traction motor bearings in railway applications

Ahmadov Heybatulla, Manafov Elshan, Guliyev Huseyngulu, Huseynov Farid

Transport Problems

2025

T. 20, z. 2

73--84

DOI: 10.20858/tp.2025.20.2.06 Keywords: traction motor bearings; fault diagnosis; fuzzy products; linguistic variables; expert system Heybatulla AHMADOV1, Elshan MANAFOV2*, Huseyngulu GULIYEV3, Farid HUSEYNOV4 A fuzzy logic-based multi-sensor diagnostic system for traction motor bearings in railway applications This article focuses on the diagnosis of the bearings of the traction motors of electric railway and subway trains. One of the main sources of mechanical failures in a traction motor is its bearings. The failure of traction motor bearings, the factors that cause these failures, and the diagnostic methods for detecting them are investigated. At this time, faults in traction motor bearing monitoring systems are determined only by temperature. In this work, it is proposed to use a system with temperature, vibration, and noise to determine the technical condition of bearings. Such a multi-parameter system, unlike traditional ones, will help determine specific defects at an early stage. The expert system’s model, based on fuzzy logic and diagnostic parameters, can accurately predict the likelihood of bearing faults in real-time under changing operating conditions. A fuzzy expert system represents knowledge in the form of fuzzy productions and linguistic variables. The expert system model was developed using the Mamdani fuzzy inference algorithm of the Fuzzy Logic Toolbox package in the MATLAB computing environment. The application of fuzzy logic in generating a knowledge base and inference processes enables the formalization of a process for evaluating technical conditions based on incomplete, faulty, and potentially erroneous information and for making decisions about fault identification.