Abstract
Objective: Diagnostics of malfunctions of rolling bearings of an asynchronous traction electric motor (ATEM) of locomotives using artifi cial neural networks. Methods: To control and diagnose the technical condition of the ATEM bearing units of locomotives, a hardware-software complex and data analysis methods are used. Results: We investigated the malfunctions of the ATEM rolling bearing of locomotives. The analysis of failures of locomotive bearing units is carried out. Vibration and current signals and the corresponding frequency spectra of an ATEM operating under normal conditions and with various bearing faults are considered. A model for assessing the technical condition of rolling bearings of locomotives has been developed, and the importance of anticipatory diagnostics has been substantiated, which makes it possible to identify defects in advance at the earliest stage of their development. Practical importance: The results of the research can be used in the system for diagnosing the technical condition of rolling bearings of traction electric motors of locomotives in real time.
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