TK38；TM6；TP307；TH17
国家自然科学基金资助项目(51079057，51039005)；高等学校博士学科点专项科研基金资助项目(20100142110012)
- Chen Xiaoyue1,2,Zhou Jianzhong2,Xiao Jian2,Fu Wenlong2,Zhang Weibo2,Xia Xin2，Li Chaoshun2,Zhang Yongchuan2
Chen Xiaoyue1,2,Zhou Jianzhong2,Xiao Jian2,Fu Wenlong2,Zhang Weibo2,Xia Xin2，Li Chaoshun2,Zhang Yongchuan2
(1.School of Electrical and Electronic Engineering, East China Jiaotong University Nanchang, 330013, China) (2.College of Hydropower and Information Engineering, Huazhong University of Science and Technology Wuhan, 430074, China)
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针对轴心轨迹图像的特征不易提取，传统基于图像处理的轴心轨迹识别方法普遍存在信息提取不全面、形状表征不准确、特征向量与形状映射关系不明确等问题，提出了一种模仿人眼的轴心轨迹识别方法。该方法首先模仿人的眼睛来提取轴心轨迹结构、区域和边界3方面最直观有效的信息；然后，通过有效信息的全面集成完成形状的综合准确表征，使特征向量与形状之间的对应关系足够明晰；最后，通过反向传播（back propagation, 简称BP）神经网络、径向基函数（radical basis function, 简称RBF）神经网络和概率神经网络（probabilistic neural network,简称PNN）实现汽轮机轴心轨迹的自动识别。实验表明，提出的轴心轨迹识别方法简单、高效、准确。
Shaft orbit recognition is an important approach for the vibration state judgment of steam turbines. Extracting the features of shaft orbit images is not an easy task, and the traditional feature extraction methods are not perfect in comprehensiveness, accuracy and stability. In order to overcome these problems, a feature extraction method based on imitating human eyes is proposed for the steam turbine. This method imitates human eyes to extract the most important information of the image structure, boundary and region, and realizes the shape characterization comprehensively and accurately through full integration of the information. Three intelligent classification methods are used to test the effectiveness of the proposed method, and the experimental results prove that this feature extraction method for steam turbine is simple, efficient and accurate.
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