子空间拓扑
断层(地质)
故障检测与隔离
核(代数)
计算机科学
陷入故障
算法
高斯过程
独立成分分析
过程(计算)
模式识别(心理学)
人工智能
高斯分布
数学
量子力学
操作系统
组合数学
物理
地质学
地震学
执行机构
作者
Xiangyu Kong,Zhilin Yang,Jiayu Luo,Li Hongzeng,Xi Yang
出处
期刊:IEEE Transactions on Instrumentation and Measurement
[Institute of Electrical and Electronics Engineers]
日期:2022-01-01
卷期号:71: 1-12
被引量:19
标识
DOI:10.1109/tim.2022.3150589
摘要
Independent component analysis (ICA) is a commonly used non-Gaussian process fault diagnosis method. A fault detection algorithm of kernel dynamic ICA (KDICA) has been proposed for the non-Gaussian process with dynamic and nonlinear characteristics. However, a lack of studies tackling the fault reconstruction and fault diagnosis algorithm exists. Hence, a fault reconstruction model based on KDICA is proposed in this article. In this model, a reduced fault subspace extraction method is proposed. It consists in dividing the fault subspace into the kernel dynamic independent component reduced fault subspace and the residual reduced fault subspace (RRFS). Based on the RRFS, a fault diagnosis approach is designed for online process monitoring. Using the proposed method, the computational complexity can be efficiently reduced and the specific fault type can be accurately identified. The Tennessee–Eastman process is used to verify the feasibility and efficiency of the proposed method and its fault diagnosis application.
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