# An Extremely Close Vibration Frequency Signal Recognition Using Deep Neural Networks
###### Abstract  
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. vibration detection; electric motor; deep neural network; Fiber Bragg Grating sensor +
Footnote †: journal: Applied sciences  
Article An Extremely Close Vibration Frequency Signal Recognition Using Deep Neural Networks  
[PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON]-[PERSON] Extremely Close Vibration Frequency Signal Recognition Using Deep Neural Networks  
Applicplic 26 March 2024  
Applic 28 March 2024
By capturing data from various points within the motor system, users can better analyze the complex vibration signals and promptly identify anomalies or potential issues. Thus, utilizing multiple sensors enhances the effectiveness of vibration signal detection and contributes to the overall reliability and efficiency of motor systems. Periodically identifying potential problems in motor systems is imperative for ensuring operational reliability and preventing catastrophic failures [5; 6]. Figure 1 depicts the monitoring of motor vibrations across different plants, showcasing numerous motors that are essential in large-scale industrial operations. The monitoring process involves capturing motor vibration signals from a distance, typically in office buildings away from the plant site. Employing sophisticated machine learning techniques enables precise identification of the vibration signal frequencies, aiding in the timely detection of potential motor faults. An accurately recognizing vibration signal frequencies within a specified range poses a considerable dilemma when employing the Fast Fourier Transform (FFT) algorithm as a widely used signal analysis method. The basic FFT is a discretized temporal approximation of the underlying continuous-time mathematical principles. One prominent constraint lies in the implicit assumption of a periodic and stationary signal, which may not accurately represent non-stationary or time-varying signals. Additionally, the discrete nature of the basic FFT introduces spectral leakage [7; 8], where energy from one frequency may spread into adjacent frequencies, potentially leading to misinterpretation of spectral content. Moreover, the basic FFT assumes uniformly sampled data, and deviations from this regular sampling may result in artifacts and inaccuracies. The inherent limitations of the basic FFT become evident as attempts are made to attain such satisfactory resolutions within the given frequency band. Thus, a critical observation arises from realizing that recognizing an extremely close frequency resolution below 1 Hz is vital in motor vibration signal monitoring. This raises challenges regarding computational efficiency and the practicality of acquiring and processing the data required for precise frequency analysis. In Refs. [5; 7; 8; 9; 10; 11], the basic FFT combines with other methods such as back-propagation neural network, least square method, the hybrid with discrete Fourier transform, prism signal processing, convolutional neural network, and Hilbert transform to overcome its limitations, respectively. Fiber Bragg Grating (FBG) stands out for vibration sensor applications as a valuable tool for recognizing signal frequencies [12].