摘要：For the fault diagnosis of complex industrial systems, because of the existence of nonlinearity, the multivariate statistical method using kernel functions has the problem that the diagnosis results are different due to the different selection of kernel functions. In this paper, a manifold learning method called the maximum variance unfolding is used which can find the kernel matrix for non-linear data by self learning, so it does not need to choose the kernel function artificially. But this method has difficulty processing the new data, this paper proposes an incremental improvement method of maximum variance unfolding. The normal samples are used for modeling, and then the low-dimensional space is constructed by incremental method for dimension reduction of the detected samples, in which the monitoring statistics are utilized to complete the fault detection. Finally, this method is applied to the fault diagnosis of subsea control system, and the feasibility of this method is verified by simulation analysis.
© 2020, Editorial Department of Control Theory & Applications South China University of Technology. All right reserved.

ISSN号：1000-8152

卷、期、页：v 37,n 4,p855-862

发表日期：2020-04-01

收录情况：EI（工程索引）

发表期刊名称：Kongzhi Lilun Yu Yingyong/Control Theory and Applications

通讯作者：贾创

第一作者：左信,高小永

论文类型：期刊论文

论文概要：贾创,左信,高小永，Fault diagnosis of the subsea control system based on incremental maximum variance unfolding，Kongzhi Lilun Yu Yingyong/Control Theory and Applications，2020，v 37,n 4,p855-862

论文题目：Fault diagnosis of the subsea control system based on incremental maximum variance unfolding