Е-Публикации
Технически университет - София

Abstract: his paper presents the development of a noninvasive vibration monitoring system for electrical equipment. Existing vibration monitoring approaches are reviewed. To enable contactless measurement, high-speed video acquisition of operating equipment is employed, followed by displacement analysis of reference points or regions on the machine casing. Technical constraints and requirements for the applied hardware are discussed. Reference regions are detected using a YOLO neural network trained via transfer learning. Key point localization is performed using the Shi–Tomasi method, supported by infrared fiducial markers. The acquired images are preprocessed to enhance contrast and suppress noise using a cascade of Gaussian and median filters. Robustness under varying operating and lighting conditions is achieved through synchronized infrared illumination. Motion tracking is implemented with a modified pyramidal Lucas–Kanade optical flow algorithm combined with the inverse-compositional Gauss–Newton method, achieving metrological precision down to subpixel displacements. Kinematic parameters are computed via central differences with Savitzky–Golay filtering. Spectral analysis is carried out using the fast Fourier transform and Morlet wavelet transform, preceded by adaptive Kalman filtering and threshold-based outlier suppression.

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