基于多特征融合和WOA-SVM的锅炉火焰燃烧状态识别  

Boiler Flame Combustion State Identification Based on Multi-feature Fusion and WOA-SVM

作　　者：卞建忠 谢炳浦 吴昊 邬士火 方虹 Bian Jianzhong;Xie Bingpu;Wu Hao;Wu Shihuo;Fang Hong(Huzhou Special Equipment Inspection Center,Huzhou 313000)

机构地区：[1]湖州市特种设备检测研究院,湖州313000

出　　处：《中国特种设备安全》2024年第9期13-18,54,共7页China Special Equipment Safety

基　　金：湖州市公益性应用研究项目(2022GZ09);湖州市特种设备检测研究院科研项目(HSQS/QR-339)。

摘　　要：针对传统火焰图像识别方法识别锅炉燃烧状态存在特征提取不充分的问题,本文提出一种基于多特征融合和鲸鱼算法优化支持向量机的锅炉火焰燃烧状态识别方法。采用梯度方向直方图、局部二值模式和尺度不变特征变换描述火焰图像特点,融合提取特征以提升特征表达能力。将融合特征作为支持向量机分类器的输入,通过鲸鱼算法优化模型参数,提高分类准确率。实验证明,多特征融合显著提升分类结果准确率,经优化的支持向量机模型最终准确率达96.64%。Aiming at the problems of insufficient feature extraction and the dependence of model parameters on manual experience in the traditional method of identifying boiler combustion state by using flame images,this paper proposes a method of identifying boiler flame combustion state based on multi-feature fusion and whale algorithm optimized support vector machine.In order to characterize the flame image from different aspects,the method firstly extracts the contour features,texture features and local features of the boiler flame image using Histogram of Orientation Gradient,Local Binary Pattern and Scale Invariant Feature Transform,respectively.Then,the three extracted features are fused to achieve complementarity between different features and to improve the feature representation of the flame image.Finally,the fusion features are used as the input sample of the support vector machine classifier for flame combustion status recognition,and the whale algorithm is used to optimize the parameters in the support vector machine to improve the accuracy of the classification model.The experimental results show that the classification accuracy of multi-feature fusion is significantly improved compared with the classification results of single feature,and the final classification accuracy of the support vector machine model optimized by the whale algorithm is as high as 96.64%.

关 键 词：火焰图像 锅炉燃烧状态识别 多特征融合 鲸鱼算法 支持向量机 

分 类 号：X933.2[环境科学与工程—安全科学]