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基于分段加权最小二乘支持向量机故障诊断的实现

发表时间:2016-05-31  浏览量:1823  下载量:587
全部作者: 吕宁,颜鲁齐
作者单位: 哈尔滨理工大学自动化学院
摘 要: 在啤酒发酵过程中,为建立精准的传感器温度故障诊断模型,在标准支持向量机(support vector machine,SVM)的基础上提出分段加权最小二乘支持向量机(weighted least square support vector machine,WLS-SVM)的方法。该方法首先利用模糊C 均值(fuzzy C-means,FCM)聚类对样本进行聚类分析,达到划分发酵阶段和建立局部模型的目的,然后应用WLS-SVM 的方法对各类样本进行建模。实验结果表明,使用该方法建立的啤酒发酵过程温度故障诊断模型具有较高的准确性。经过比较,该方法建立模型的泛化能力要强于其他SVM方法建立的模型。
关 键 词: 自动控制理论;支持向量机;模糊C 均值聚类;加权最小二乘支持向量机;啤酒发酵;建模
Title: Realization of fault diagnosis based on piecewise weighted least squares support vector machine
Author: LÜ Ning, YAN Luqi
Organization: College of Automation, Harbin University of Science and Technology
Abstract: In the process of beer fermentation, in order to establish the precise temperature sensor fault diagnosis model, on the basis of standard support vector machine (SVM), we proposed piecewise weighted least square support vector machine (WLS-SVM) method. The method was first using fuzzy C-means clustering (FCM) of the sample of poly class analysis, to divide fermentation stage and the establishment of local model. Then, using the WLS-SVM method is used for modeling of various types of samples. The experimental results show that the model has a high accuracy in the process of temperature fault diagnosis of beer fermentation process. After comparison, the proposed method establishes the model's generalization ability better than other SVM methods to build the model.
Key words: autocontrol theory; support vector machine; fuzzy C-means clustering; weighted least squares support vector machine; beer fermentation; modeling
发表期数: 2016年5月第10期
引用格式: 吕宁,颜鲁齐. 基于分段加权最小二乘支持向量机故障诊断的实现[J]. 中国科技论文在线精品论文,2016,9(10):1048-1054.
 
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