基于聚类支持向量机的船用污水处理装置故障诊断  

Fault diagnosis for ship sewage treatment equipment based on clustering support vector machine

作　　者：曹晓莉[1] 江朝元[1] 甘思源[2] 

机构地区：[1]重庆工商大学计算机科学与信息工程学院 [2]重庆大学自动化学院,重庆400030

出　　处：《计算机应用》2008年第10期2648-2651,共4页journal of Computer Applications

基　　金：教育部工程研究中心重大项目(07011301);重庆市教委资助项目(070707)

摘　　要：针对船用污水处理装置状态监测与故障诊断问题,提出了一种聚类支持向量机的故障诊断算法模型。该算法模型首先采用神经网络聚类算法将设备监测状态样本空间聚类分析出正常与异常子空间,再对异常子空间构造多分类支持向量机对故障进行诊断识别。该算法模型避免了盲目故障分类,提高了分类性能。通过对某船用污水处理装置实测样本的训练和检验表明,该算法具有较好的泛化性和推广能力。To solve the problems of condition monitoring and fault diagnosis for ship sewage treatment equipment, a fault diagnosis model based on clustering Support Vector Machines （SVM） was proposed. In the model, the neural network clustering algorithm was used to realize clustering analysis and to obtain the normal subspaces and the abnormal subspaces in the condition monitoring sample space. Then, to the abnormal subspaces, the multi-classification SVM based on binary tree architecture was designed to carry out the fault diagnosis and recognition. Compared with the traditional SVM learning algorithms, the proposed algorithm avoided the blind classification and improved the classification performance in some extent. The model was applied to a ship sewage treatment equipment to train and exam the measured samples. Experiment results show this method has good generalization and expendibility.

关 键 词：支持向量机 故障诊断 聚类 分类 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程]