Multi-subspace factor analysis integrated with support vector data description for multimode process monitoring

DOI10.1016/j.jfranklin.2018.07.044zbMath1398.93234OpenAlexW2885361421MaRDI QIDQ1797206

Zhichao Li, Bei Wang, Xuefeng Yan

Publication date: 18 October 2018

Published in: Journal of the Franklin Institute (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.jfranklin.2018.07.044

zbMATH Keywords

support vector data descriptionmultimode process monitoringmulti-subspace factor analysis

Mathematics Subject Classification ID

Factor analysis and principal components; correspondence analysis (62H25) Learning and adaptive systems in artificial intelligence (68T05) Control/observation systems involving computers (process control, etc.) (93C83) Pattern recognition, speech recognition (68T10)

Related Items (5)

Decentralized proximal splitting algorithms for composite constrained convex optimization ⋮ Canonical correlation analysis-based explicit relation discovery for statistical process monitoring ⋮ Multivariate statistical process monitoring based on principal discriminative component analysis ⋮ Parallel supervised additive and multiplicative faults detection for nonlinear process ⋮ Primal-dual stochastic distributed algorithm for constrained convex optimization

Cites Work

• Nonlinear principal component analysis and its applications
• Support vector data description
• Probabilistic Principal Component Analysis
• Fault detection and diagnosis in industrial systems

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