An Intelligent Area Localization Framework for Rotating Machine Vision Vibration Measurement

Abstract

Vision-based vibration measurement technology has received extensive attention due to its advantages of non-contact, high spatial resolution, and no-load effect. However, with the complexity of measurement objects and measurement tasks, the existing visual measurement technology is gradually showing greater limitations. Specifically, due to the uncertainty of actual working conditions, not all pixels in the field of view can measure vibration. Therefore, the selection of measurement points needs to rely on prior structural information and artificial experience. Frequent manual point selection tests bring a lot of resource consumption, which greatly reduces the automation degree of visual vibration measurement. This paper focuses on an intelligent area localization method for vibration measurement of rotating machine vision and designs a deep learning-based vibration measurement area localization framework to directly feedback all reliable measurement pixels from image data, which is called the VMAL framework. Firstly, the sub-pixel physical feature information associated with vibration in the data is analyzed through an unsupervised image decomposition network, and then a regularized regional localization network is used to cluster and output reliable regional pixels. Experimental results on a medium-sized single-span rotor platform verify the effectiveness of the proposed method.