基于计算机视觉的管路振动感知算法

为解决电厂生产区域中管路振动超出正常范围未能及时预警而导致管路破损或连接法兰、阀门处出现工质泄漏的问题,提出了一种基于计算机视觉的管路振动感知算法,首先采用卷积神经网络估计待测管路的光流信息,然后通过分析光流信息检测出管路是否振动,接着通过振动测量模块对监测画面中检测出振动的管路目标的振动频率和振幅进行测量,从而实现对管路振动的感知.在电厂原有摄像头拍摄的振动管路数据上进行了实验,测试结果表明本文方法的速度约为4 f/s,振动频率的测量误差小于0.08.为计算机视觉技术在不改变电厂原有硬件装置的情况下实现实时管路振动检测和测量任务提供了新的思路.

Pipeline Vibration Perception Algorithm Based on Computer Vision

A pipeline vibration perception algorithm based on computer vision is proposed to solve pipeline damage or working fluid leakage at connecting flanges and valves due to a failure of timely warning induced by abnormal pipeline vibration in the production area of power plants. First, a convolutional neural network is used to estimate the optical flow information of the pipeline to be measured. Then, the information is analyzed to detect whether the pipeline vibrates or not. Finally, a vibration measurement module is employed to measure the vibration frequency and amplitude of the vibrating pipeline target in the monitor display for the perception of pipeline vibration. The experiments on the vibrating pipeline data taken by the original camera of a power plant show that the speed of the proposed method is about 4 f/s, and the measurement error of vibration frequency is less than 0.08. This method provides new ideas for computer vision technology to accomplish real-time pipeline vibration detection and measurement tasks without changing the original hardware devices of the power plants.