Matches in SemOpenAlex for { <https://semopenalex.org/work/W3120377913> ?p ?o ?g. }
Showing items 1 to 86 of
86
with 100 items per page.
- W3120377913 abstract "Process monitoring is a challenging task for modern industrial processes which are commonly nonstationary in nature, revealing typical non-Gaussian characteristics. Nowadays, data-driven based fault detection methods have drawn increasing attention, most of which work under an assumption that the process is subject to Gaussian distribution. But in practice, the underlying non-Gaussian characteristics may be typical in the complex process, which cannot be properly enclosed by a statistical model with a close confidence region and thus may be insensitive to fault detection. Hence, it is necessary to explore and separate the underlying Gaussian and non-Gaussian distributions in fine-grain. In this work, a Gaussian and non-Gaussian subspace decomposition method is proposed by designing a variant of stationary subspace analysis (VSSA) for nonstationary process monitoring. First, the whole time-wise nonstationary process can be neatly converted to condition-wise slices. Then, a Monte Carlo sampling based VSSA technique is designed to separate Gaussian and non-Gaussian subspaces from each other, which focuses on analyzing sample distribution rather than time series properties. Here the Gaussian subspace, which is readily characterized by a statistical model, is used for revealing similar condition slices and affiliate them into the same condition mode. And two monitoring statistics are developed to explore the Gaussian and non-Gaussian distribution structures, thus providing fine-grained distribution analytics and promoting monitoring performance. The feasibility and performance of the proposed method are demonstrated on a real thermal power plant process." @default.
- W3120377913 created "2021-01-18" @default.
- W3120377913 creator A5006738371 @default.
- W3120377913 creator A5038929132 @default.
- W3120377913 creator A5077280762 @default.
- W3120377913 date "2020-12-13" @default.
- W3120377913 modified "2023-09-27" @default.
- W3120377913 title "Fault detection for Nonstationary Process with Decomposition and Analytics of Gaussian and Non-Gaussian Subspaces" @default.
- W3120377913 cites W1965555277 @default.
- W3120377913 cites W1991410152 @default.
- W3120377913 cites W2015436473 @default.
- W3120377913 cites W2031329197 @default.
- W3120377913 cites W2043261186 @default.
- W3120377913 cites W2051385221 @default.
- W3120377913 cites W2051940462 @default.
- W3120377913 cites W2056871913 @default.
- W3120377913 cites W2071128523 @default.
- W3120377913 cites W2094687520 @default.
- W3120377913 cites W2123649031 @default.
- W3120377913 cites W2143822727 @default.
- W3120377913 cites W2441266639 @default.
- W3120377913 cites W2514000312 @default.
- W3120377913 cites W2772343646 @default.
- W3120377913 cites W2883667036 @default.
- W3120377913 cites W2886478610 @default.
- W3120377913 cites W2898848825 @default.
- W3120377913 cites W2939985247 @default.
- W3120377913 cites W3047463300 @default.
- W3120377913 cites W3104298728 @default.
- W3120377913 cites W562231173 @default.
- W3120377913 doi "https://doi.org/10.1109/icarcv50220.2020.9305438" @default.
- W3120377913 hasPublicationYear "2020" @default.
- W3120377913 type Work @default.
- W3120377913 sameAs 3120377913 @default.
- W3120377913 citedByCount "0" @default.
- W3120377913 crossrefType "proceedings-article" @default.
- W3120377913 hasAuthorship W3120377913A5006738371 @default.
- W3120377913 hasAuthorship W3120377913A5038929132 @default.
- W3120377913 hasAuthorship W3120377913A5077280762 @default.
- W3120377913 hasConcept C11413529 @default.
- W3120377913 hasConcept C119857082 @default.
- W3120377913 hasConcept C121332964 @default.
- W3120377913 hasConcept C12362212 @default.
- W3120377913 hasConcept C154945302 @default.
- W3120377913 hasConcept C163716315 @default.
- W3120377913 hasConcept C2524010 @default.
- W3120377913 hasConcept C32834561 @default.
- W3120377913 hasConcept C33923547 @default.
- W3120377913 hasConcept C41008148 @default.
- W3120377913 hasConcept C51267290 @default.
- W3120377913 hasConcept C61326573 @default.
- W3120377913 hasConcept C62520636 @default.
- W3120377913 hasConcept C7218915 @default.
- W3120377913 hasConcept C81692654 @default.
- W3120377913 hasConceptScore W3120377913C11413529 @default.
- W3120377913 hasConceptScore W3120377913C119857082 @default.
- W3120377913 hasConceptScore W3120377913C121332964 @default.
- W3120377913 hasConceptScore W3120377913C12362212 @default.
- W3120377913 hasConceptScore W3120377913C154945302 @default.
- W3120377913 hasConceptScore W3120377913C163716315 @default.
- W3120377913 hasConceptScore W3120377913C2524010 @default.
- W3120377913 hasConceptScore W3120377913C32834561 @default.
- W3120377913 hasConceptScore W3120377913C33923547 @default.
- W3120377913 hasConceptScore W3120377913C41008148 @default.
- W3120377913 hasConceptScore W3120377913C51267290 @default.
- W3120377913 hasConceptScore W3120377913C61326573 @default.
- W3120377913 hasConceptScore W3120377913C62520636 @default.
- W3120377913 hasConceptScore W3120377913C7218915 @default.
- W3120377913 hasConceptScore W3120377913C81692654 @default.
- W3120377913 hasLocation W31203779131 @default.
- W3120377913 hasOpenAccess W3120377913 @default.
- W3120377913 hasPrimaryLocation W31203779131 @default.
- W3120377913 hasRelatedWork W1551381384 @default.
- W3120377913 hasRelatedWork W2069479424 @default.
- W3120377913 hasRelatedWork W2351615707 @default.
- W3120377913 hasRelatedWork W3109073254 @default.
- W3120377913 hasRelatedWork W3120377913 @default.
- W3120377913 hasRelatedWork W3197566623 @default.
- W3120377913 hasRelatedWork W3216458018 @default.
- W3120377913 hasRelatedWork W4298202464 @default.
- W3120377913 hasRelatedWork W4311866757 @default.
- W3120377913 hasRelatedWork W4312290701 @default.
- W3120377913 isParatext "false" @default.
- W3120377913 isRetracted "false" @default.
- W3120377913 magId "3120377913" @default.
- W3120377913 workType "article" @default.