基于特征空间自适应k近邻工业过程故障检测A feature space adaptive k-nearest neighbor method for industrial fault detection
郭小萍,徐月,李元
摘要(Abstract):
针对工业生产过程故障检测模型不能及时更新的问题,提出了一种特征空间自适应k近邻(featurespace adaptive k-nearest neighbor,FS-AkNN)故障检测方法。首先利用主元分析对训练数据进行降维,构建特征空间,然后利用k最近邻方法建立故障检测模型。在过程监视过程中,提出了基于距离规则的自适应更新故障检测模型。通过一个数值例子和TE过程的仿真实验结果表明了该方法的有效性。
关键词(KeyWords): 自适应;k近邻(kNN);主元分析;故障检测
基金项目(Foundation): 国家自然科学基金(61673279);国家自然科学基金重大项目(61490701);; 辽宁省科学事业公益研究基金(2016001006)
作者(Author): 郭小萍,徐月,李元
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