Fault detection and diagnosis of non-linear non-Gaussian dynamic processes using kernel dynamic independent component analysis

DOI10.1016/J.INS.2013.06.021zbMath1329.62275OpenAlexW2059891946MaRDI QIDQ903581

Youqing Wang, Jicong Fan

Publication date: 14 January 2016

Published in: Information Sciences (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.ins.2013.06.021

zbMATH Keywords

independent component analysisnonlinear contribution plotnonlinear non-Gaussian dynamic processesTE process

Mathematics Subject Classification ID

Factor analysis and principal components; correspondence analysis (62H25)

Related Items (6)

A novel multivariate statistical process monitoring algorithm: orthonormal subspace analysis ⋮ Dynamic nonlinear process monitoring based on dynamic correlation variable selection and kernel principal component regression ⋮ Robust fault detection for a class of uncertain nonlinear systems based on multiobjective optimization ⋮ Canonical correlation analysis-based explicit relation discovery for statistical process monitoring ⋮ Simultaneous fault diagnosis for robot manipulators with actuator and sensor faults ⋮ Sparse subspace clustering for data with missing entries and high-rank matrix completion

Cites Work

• Scaling the kernel function based on the separating boundary in input space: a data-dependent way for improving the performance of kernel methods
• A fast fault-identification algorithm for bijective connection graphs using the PMC model
• Complex system fault diagnosis based on a fuzzy robust wavelet support vector classifier and an adaptive Gaussian particle swarm optimization
• 10.1162/153244303322753706
• Multivariate SPC Charts for Monitoring Batch Processes
• Fault detection and diagnosis in industrial systems

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