# An Extremely Close Vibration Frequency Signal Recognition Using Deep Neural Networks
###### Abstract
To make the sensor more sensitive, the techniques in [13; 14; 15; 16] enhance the FBG's structure and utilize a more significant number of FBGs. FBG sensing has been applied in various areas, such as detecting earthquakes, fault  
Figure 1: The scenario of vibration signal measurement using a DNN approach for electric machines condition monitoring. (DNN: Deep neural networks, M: Motor, S: Sensor). detections, water level monitoring, flowmeter applications, measuring inclinations, health monitoring, and damage location determination [17; 18; 19; 20; 21; 22; 23]. The perks of FBG sensors, such as being resistant to electromagnetic interference, small size, and flexibility, make them a promising option for advanced vibration detection. This is especially important in intricate systems with multiple motors, emphasizing FBG's crucial role as a vibration sensor [24; 25]. In discerning optical fiber vibration sensing, utilizing Deep Neural Networks (DNN) stands out as a methodology with distinct advantages. Previous investigations into this domain have witnessed the employment of various controllers, including random forest, convolutional neural networks (CNN) + support vector machine (SVM), adaptive filtering CNN, reinforcement learning, SVM, fractional-order PID, YOLO, and XGBoost [11; 26; 27; 28; 29; 30; 31; 32]. While demonstrating in extremely close frequency scenarios, these controllers have not addressed vibration signal frequency recognition. This gap is urgent due to the importance of accurately detecting such frequencies, particularly in applications like electric motor monitoring and fault detection. Failure to address this issue may lead to erroneous diagnoses and overlooked anomalies, potentially resulting in costly downtime, equipment damage, or safety hazards. Therefore, there is a pressing need to develop and implement robust controllers that can effectively handle the challenges posed by extremely close frequency scenarios, ensuring reliable and precise vibration signal frequency recognition in engineering systems. Understanding the crucial aspects often overlooked in discussions related to conventional signal analysis and common machine learning approaches, a new novel system is proposed in this study. The proposed integration of an FBG structure as an optical fiber vibration sensing method for detecting extremely close frequencies in vibration signals from electric motors marks a significant advancement in condition monitoring. The contributions of this study are described as follows:  
* To recognize extremely close frequencies of vibration signals without any signal analysis methods often entails intricate mathematical calculations and protracted computation times;
* To demonstrate an efficient and meaningful alternative of the DNN model for accurately recognized vibration signal frequency with a simple FBG configuration system;
* To demonstrate that the proposed system is adaptable to various other types of vibration sensors. The image-based vibration signal recognition system embedded in DNN holds potential applicability beyond its immediate scope. Furthermore, its versatility extends to detecting and analyzing acoustic signals, broadening its utility across multiple signal processing and analysis domains. This study introduces an innovative perspective in vibration signal analysis by presenting a novel merger of a simple FBG technology and DNN for detecting extremely close frequencies within motor vibrations, an approach hitherto unexplored in the existing literature. The proposed system configuration is designed to acquire vibration signals with exceptional proximity and high precision, with its underlying concepts drawing inspiration from the diagram block presented in [33]. Furthermore, a deep learning approach is employed to ensure robust precision in vibration measurements, as illustrated in Figure 2. This system configuration is applicable in on-site production plants, energy plants, utility plants, etc., that consist of a lot of vibration from any source.