Intelligent Vibration Monitoring System for Smart Industry Utilizing Optical Fiber Sensor Combined with Machine Learning
Pradeep Kumar, Guo-Liang Shih, Cheng-Kai Yao, Stotaw Talbachew Hayle, Yibeltal Chanie Manie, Peng‐Chun Peng
Abstract
In this paper, we proposed and experimentally demonstrated the association of a fiber Bragg Grating (FBG) sensing system with You Only Look Once V7 (YOLO V7) to identify the vibration signal of a faulty machine. In the experiment, the YOLO V7 network architecture consists of a backbone, three detection heads (Headx3), a path aggregation network (PAN), and a feature pyramid network (FPN). The proposed architecture has an FBG sensor and the FBG interrogator employed for collecting sensing vibration signals or vibration data when degradation or fault occurs. An FBG interrogator collects vibration data independently, and then the YOLO V7 object detection algorithm is the recognition architecture of the vibration pattern of the signal. Thus, the proposed vibration recognition or detection is an assurance for detecting vibration signals that can support monitoring the machine’s health. Moreover, this research is promising for ensuring a high accuracy detection of faulty signals rate in industrial equipment monitoring and offers a robust system, resulting in remarkable accuracy with an overall model accuracy of 99.7%. The result shows that the model can identify the faulty signal more accurately and effectively detect the faulty vibration signal using the detection algorithm.