Concurrent quality and process monitoring with canonical correlation analysis

Zhu, Qinqin; Liu, Qiang; Qin, S. Joe

doi:10.1016/j.jprocont.2017.06.017

Cited by 94 publications

(43 citation statements)

References 18 publications

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“…Finalmente, por medio del software Rstudio versión 1.3.1073, se realizó un ACC (Análisis de Correspondencia Canónica), que es una técnica de análisis de datos que extrae la estructura de correlación multidimensional entre dos conjuntos de variables (Qinqin & Qin, 2017). El ACC fue empleado para determinar la correlación existente entre los parámetros fisicoquímicos, los índices de calidad, las familias identificadas y los índices de Shannon y de Simpson, en cada uno de los sitios y temporadas de muestreo a partir de bases de datos estructuradas por sitios y por temporada de muestreo.…”

Section: Hidrobiológicaunclassified

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2021

Hidrobiológica

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El clima es semiseco con lluvias en verano, la temperatura media anual es de 16.6 ± 2.5°C, la precipitación anual es de 477.6 ± 119.9 are 504 wells, of which 61 hm 3 /year are extracted. The main water pathways are fractured rocks and the granular medium. Conclusions. The location of the springs and swamps is related to geological contact zones between different tectonic structures, they present diverse characteristics that indicate local flows and one of intermediate type; the decrease or loss of flow is associated with the opening of nearby wells. Management proposals are proposed towards the conservation of vegetation and biodiversity in the sub-basin to recover water performance at different time scales.

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Section: Hidrobiológicaunclassified

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2021

Hidrobiológica

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“…CCAs are widely employed in practical analysis. For example, CCA was used to examine the relationship between quality and process monitoring combined with a regularization method [25]. In addition, CCAs are widely used in biomedicine [26,27].…”

Section: Canonical Correlation Analysismentioning

confidence: 99%

Canonical Correlation Study on the Relationship between Shipping Development and Water Environment of the Yangtze River

et al. 2020

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The sustainable development of the Yangtze River will affect the lives of the people who live along it as well as the development of cities beside it. This study investigated the relationship between shipping development and the water environment of the Yangtze River. Canonical correlation analysis is a multivariate statistical method used to study the correlation between two groups of variables; this study employed it to analyze data relevant to shipping and the water environment of the Yangtze River from 2006 to 2016. Furthermore, the Yangtze River Shipping Prosperity Index and Yangtze River mainline freight volume were used to characterize the development of Yangtze River shipping. The water environment of the Yangtze River is characterized by wastewater discharge, ammonia nitrogen concentration, biochemical oxygen demand, the potassium permanganate index, and petroleum pollution. The results showed that a significant correlation exists between Yangtze River shipping and the river’s water environment. Furthermore, mainline freight volume has a significant impact on the quantity of wastewater discharged and petroleum pollution in the water environment.

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“…The second part is the PCs selection. Because a time delay [28] is introduced in the first part, the original space increase several times. We adopt the ReliefF algorithm to select the slow features sensitive to fault identification, establish the relevant main and residual subspace [29], and achieve dimensional reduction.…”

Section: Dynamic Related Relieff Slow Feature Analysismentioning

confidence: 99%

“…In the establishment of the fault monitoring model based on the DRRSFA method, the dynamic process order determined by the Akaike Information Standard (AIC) is 6, so the time delay q = 6 is selected of the heat treatment furnace process. The Hankel matrix can be constructed by Equation (27) and (28), where the number of columns N is 989 and the number of rows mq is 216. Then through the projection transformation, we obtain 216 slow features, which are put into ReliefF algorithm to get PCs.…”

Section: B Case Study Of the Actual Production Process Datamentioning

confidence: 99%

A Novel Process Monitoring Method Based on Dynamic Related ReliefF-SFA Method

Xiaozhi

Zhang

et al. 2020

IEEE Access

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This paper proposes a condition monitoring method based on Dynamic Related ReliefF-SFA (DRRSFA), which solves the problem of huge variable dimensions and strong autocorrelation in process monitoring. First, the samples are mapped into a new space, and the slow projection components (SPCs) of the samples are calculated. The SPCs not only considers the autocorrelation between the variables, but also characterizes the inherent properties of the whole system. Second, the feature selection algorithm ReliefF is used to select the more weighted features, which is called the principal components(PCs) to achieve dimensionality reduction. Then the corresponding statistics and control limits are calculated based on the obtained PCs. Finally, the process monitoring using the proposed algorithm is performed by testing a numerical example and the actual production process data, and the results show the effectiveness of the proposed method. INDEX TERMS Process monitoring, slow feature analysis, slow projection components, ReliefF algorithm, projection components.

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Concurrent quality and process monitoring with canonical correlation analysis

Cited by 94 publications

References 18 publications

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Canonical Correlation Study on the Relationship between Shipping Development and Water Environment of the Yangtze River

A Novel Process Monitoring Method Based on Dynamic Related ReliefF-SFA Method

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