DETERMINATION OF THE TECHNICAL CONDITION OF LOCOMOTIVE BEARING ASSEMBLIES USING VIBRATION DIAGNOSTIC METHODS
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Abstract
This paper investigates the determination of the technical condition of locomotive bearing assemblies using vibration diagnostic methods. The major types of bearing faults, methods for identifying their characteristic vibration signatures, and modern diagnostic technologies are comprehensively analyzed. Particular attention is given to the analysis of vibration signals in both the time and frequency domains, highlighting the diagnostic capabilities of advanced signal-processing techniques such as the Fast Fourier Transform (FFT), Envelope Analysis, and the Wavelet Transform. Furthermore, the paper discusses the improvement of maintenance strategies based on the concepts of Condition-Based Maintenance (CBM) and Predictive Maintenance (PdM), as well as the advantages of artificial intelligence-based diagnostic systems for automated fault detection and condition assessment. The research findings demonstrate that vibration diagnostics is an effective tool for the early detection of defects in locomotive bearing assemblies, enhancing operational reliability, reducing maintenance costs, and improving the overall efficiency of railway rolling stock maintenance.
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References
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