Neural Network with Confidence Kernel for Robust Vibration Frequency Prediction

Abstract

Image-based measurement has received increasing attention as it can substantially reduce the cost of labor, measurement equipment, and installation process. Instead of using optical flow, pattern, or marker tracking to extract a displacement signal, in this study, a novel noncontact machine learning-based system was proposed to directly predict vibration frequency with high accuracy and good reliability by using image sequences acquired from a single camera. The performance of the proposed method was demonstrated through experiments conducted in a laboratory and under real-field conditions and compared with those obtained using a contacted sensor. The vibration frequency prediction results of the proposed method are compared with industry-level vibration sensor results in the frequency domain, demonstrating that the proposed method could predict the target-object-vibration frequency as accurately as an industry-level vibration sensor, even under uncontrollable real-field conditions with no additional enhancement or extra signal processing techniques. However, only the principal vibration frequency of a measurement target is predicted, and the measurement range is limited by the trained model. Nonetheless, if these limitations are resolved, this method can potentially be used in real engineering applications in mechanical or civil structural health monitoring thanks to the simple deployment and concise pipeline of this method.