Mentari Putri Jati, Muhammad Irfan Luthfi, Cheng-Kai Yao, Amare Mulatie Dehnaw, Yibeltal Chanie Manie, Peng-Chun Peng
This study proposes the utilization of an optical fiber vibration sensor for detecting the superposition of extremely close frequencies in vibration signals. Integration of deep neural networks (DNN) proves to be meaningful and efficient, eliminating the need for signal analysis methods involving complex mathematical calculations and longer computation times. Simulation results of the proposed model demonstrate the remarkable capability to accurately distinguish frequencies below 1 Hz. This underscores the effectiveness of the proposed image-based vibration signal recognition system embedded in DNN as a streamlined yet highly accurate method for vibration signal detection, applicable across various vibration sensors. Both simulation and experimental evaluations substantiate the practical applicability of this integrated approach, thereby enhancing electric motor vibration monitoring techniques. © 2024 by the authors.
Department of Electro-Optical Engineering, National Taipei University of Technology, Taipei, 10608, Taiwan; Department of Electrical and Electronics Engineering, Vocational Faculty, Universitas Negeri Yogyakarta, Yogyakarta, 55281, Indonesia; Department of Electronics and Informatics Engineering Education, Engineering Faculty, Universitas Negeri, Yogyakarta, 55281, Indonesia
