模型水轮机空化现象智能识别方法

Intelligent identification method for cavitation phenomena in model hydroturbine

为了更加准确地判断模型水轮机是否发生空化现象,提出了一种基于图像识别的模型水轮机多态图像智能识别方法。该方法通过机器预处理及二值化处理对目标水轮机转轮图像进行特征提取,并构造目标特征矩阵;将该目标特征矩阵与经专家库得到的修正系数或修正区间相乘后输入到空化识别模型中,并与模型中存储的模板图像的修正特征矩阵进行比对,实现模型水轮机的空化识别。工程应用结果表明:该方法的识别准确率约为80%,存在少许“错杀”现象,但可以满足使用要求;相较于现有方法,该方法不仅能提高水轮机空化识别的速度,还能提高识别质量,实现水轮机空化检测的智能化、数字化、简单化。

In order to more accurately determine whether cavitation occurs in the model hydroturbine,an intelligent identification method for turbine polymorphic images is proposed based on image recognition.This method extracts features from the target turbine runner images through machine preprocessing and binarization,and constructs the target feature matrix of the target image.The target feature matrix is multiplied by the correction value obtained from the expert database experience and is input into the cavitation identification model.It is then compared with the template correction feature matrix of the template images stored in the model to achieve cavitation identification of the water turbine runner.The practical application results of the project show that the identification accuracy of this method is about 80%,with a slight occurrence of false positives,but it can meet the requirements for practical use.Compared with existing methods,this method can not only improve the speed of turbine cavitation identification,but also enhance the identification quality,thereby achieving intelligent,mathematical,and simplified turbine cavitation detection.