Visual dynamic model based on self-organizing maps for supervision and fault detection in industrial processes

Fuertes, Juan J.; Domí­nguez, Manuel; Reguera, P.; Prada, Miguel A.; Díaz, Ignacio; Cuadrado, Abel A.

doi:10.1016/j.engappai.2009.06.001

Cited by 34 publications

(16 citation statements)

References 18 publications

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“…In both cases, large datasets make a simple analysis difficult. The information content of the dataset can, however, provide important guidelines for decision makers using data visualization methods (Fuertes et al 2010).…”

Section: Rebecca M Page and Jelena Simovic Rotamentioning

confidence: 99%

Urban Geology

Huggenberger¹,

Epting²

2011

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Section: Rebecca M Page and Jelena Simovic Rotamentioning

confidence: 99%

Urban Geology

Huggenberger¹,

Epting²

2011

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“…Hazardous system states need to be rapidly identified, a task which requires the operations manager to be able to view and assess the current system state and decide on the best course of action within a short period of time before any damage has been done. The complexity is also increased, as these states can be induced by one or more measured parameters indicating a deviation from normal, or desired situations (Camplani et al 2009;Fuertes et al 2010). The identification and interpretation of significant fluctuations in time series requires time and knowledge, a complex task ideally automated so that the operator of the process under surveillance can select the steps necessary to steer the system back to the desired state.…”

Section: Introductionmentioning

confidence: 97%

“…One method used to identify and track system states are Self-Organizing Maps (SOM), which are a form of artificial neural networks (ANN) (Kohonen 2001;Mustonen et al 2008;Fuertes et al 2010). These ANNs are able to extract the inherent structure, i.e., the underlying patterns, directly from a data set without an explicit physical model by resolving nonlinear input-output relationships in complex systems.…”

Section: Introductionmentioning

confidence: 99%

Multivariate Analysis of Groundwater-Quality Time-Series Using Self-organizing Maps and Sammon’s Mapping

Page

Huggenberger

Lischeid

2015

Water Resour Manage

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Groundwater extracted from alluvial aquifers close to rivers is vulnerable to contamination by infiltrating river water. Infiltration is often increased during high discharge events, when the levels of waterborne pathogens are also increased. Water suppliers with low-level treatment thus rely on alternative measures derived from information on system state to manage the resource and maintain drinking-water quality. In this study, a combination of Self-Organizing Maps and Sammon's Mapping (SOM-SM) was used as a proxy analysis of a multivariate time-series to detect critical system states whereby contamination of the drinking water extraction wells is imminent. Groundwater head, temperature and electrical conductivity time-series from groundwater observation wells were analysed using the SOM-SM method. Independent measurements (spectral absorption coefficient, turbidity, particle density and river stage) were used. This approach can identify critical system states and can be integrated into an adaptive, online, automated groundwater-management process.

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“…Such modern devices now inspect metal products (Fuertes et al, 2010;Barelli et al, 2008), textile fabrics (Mak et al, 2009), and pipeline (Sinha and Fieguth 2006;Guo et al, 2009) are used in trash separation processes (Maldonado and Graña, 2009), and robotics (Mitzias and Mertzios, 2004) even while products are on conveyors.…”

Section: Introductionmentioning

confidence: 98%

Visual inspection of dimensions and positions of geometrical quality characteristics

Hassan

Diab

2011

IJQI

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Image analysis techniques are being increasingly used to automate industrial inspection. The manual activity of inspection could be subjective and highly dependent on the experience of human personnel.In this work, the authors introduce a novel visual inspection approach that can be used online to test simultaneously multiple quality characteristics. The approach utilises image processing tools to deal with the product image; and extract features of its geometrical characteristics. Based on tolerance bands of each characteristic, an index is experimentally developed to reflect the deviation of a quality characteristic dimension from its nominal value; and one can decide whether a characteristic complies with the pre-specified tolerance. The index is proved to have a linear association with the deviation from the nominal sizes. The research is extended to test shifts in a quality characteristic position.

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Visual dynamic model based on self-organizing maps for supervision and fault detection in industrial processes

Cited by 34 publications

References 18 publications

Urban Geology

Urban Geology

Multivariate Analysis of Groundwater-Quality Time-Series Using Self-organizing Maps and Sammon’s Mapping

Visual inspection of dimensions and positions of geometrical quality characteristics

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