A multi-layer neural network model for detecting changes in the process mean

Cheng, Chuen‐Sheng

doi:10.1016/0360-8352(94)00024-h

Cited by 126 publications

(61 citation statements)

References 13 publications

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“…They have also been used to complement SPC (Cheng, 1995;Guh et al, 1999;Chen and Wang, 2004;Guh, 2005;Chen et al, 2007;Cheng and Cheng, 2008;Abbasi, 2009), improving previously obtained results in all cases.…”

Section: Introductionsupporting

confidence: 63%

“…1) (Garcia Fernández et al, 2008b), a variation of the hyperbolic tangent (Cheng, 1995;Chen and Wang, 2004;Cheng and Cheng, 2008). The two functions are mathematically equivalent but the hyperbolic tangent sigmoid function produces an output much more quickly, thereby improving the efficiency of the network (Demuth et al, 2002).…”

Section: Artificial Neural Networkmentioning

confidence: 99%

“…This is the most commonly used model in the references consulted, both in the field of SPC and in particleboard production control (Cook et al, 1991;Cook and Whittaker, 1993;Cheng, 1995;Bissessur et al, 1999;Chen and Wang, 2004;Sukthomya and Tannock, 2005;Cheng and Cheng, 2008;García Fernández et al, 2008b).…”

Section: Artificial Neural Networkmentioning

confidence: 99%

“…In general, the ANN is obtained by a process of trial and error (Lin and Tseng, 2000). Structures with few neurons tend to be less sensitive to small changes in the process, while adding an excessive amount of neurons to the network does not greatly improve its results (Cheng, 1995).…”

Section: Introductionmentioning

confidence: 99%

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Artificial neural networks in variable process control: application in particleboard manufacture

et al. 2009

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Artificial neural networks are an efficient tool for modelling production control processes using data from the actual production as well as simulated or design of experiments data. In this study two artificial neural networks were combined with the control process charts and it was checked whether the data obtained by the networks were valid for variable process control in particleboard manufacture.The networks made it possible to obtain the mean and standard deviation of the internal bond strength of the particleboard within acceptable margins using known data of thickness, density, moisture content, swelling and absorption. The networks obtained met the acceptance criteria for test values from non-standard test methods, as well as the criteria for using these values in statistical process control.Key words: Artificial neural networks (ANN), statistical process control (SPC), internal bond strength, wood based panels. ResumenRedes neuronales artificiales en el control de procesos por variables: aplicación en la fabricación de tableros de partículas Las redes neuronales artificiales son una herramienta eficaz para el modelado de los procesos de control de producción, tanto partiendo de datos de la propia producción como de datos simulados o procedentes de diseños de experimentos. En este estudio se han combinado dos redes neuronales artificiales con los gráficos de control de procesos y se ha comprobado si los datos obtenidos con ellas eran válidos para el control de producción por variables en la fabricación de tableros de partículas.Las redes han permitido obtener valores de la media y la desviación típica de la cohesión interna del tablero de partículas dentro de unos márgenes aceptables a partir de datos conocidos de espesor, densidad, contenido de humedad, hinchazón y absorción. Las redes obtenidas han cumplido con los requisitos de aceptación de valores de ensayo por métodos alternativos al normalizado y con los requisitos impuestos para su utilización en el control estadístico de procesos.Palabras clave: Redes neuronales artificiales (RNA), control estadístico de procesos (CEP), resistencia a la tracción interna, tableros derivados de madera.

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Section: Introductionsupporting

confidence: 63%

Section: Artificial Neural Networkmentioning

confidence: 99%

Section: Artificial Neural Networkmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Artificial neural networks in variable process control: application in particleboard manufacture

et al. 2009

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“…2 LITERATURE REVIEW Cheng [2] provided an alternative to SPC methods. The SPC methods, namely the Shewhart and CUSUM control schemes, were replaced by ANNs.…”

Section: Introductionmentioning

confidence: 99%

Detecting flow meter drift by using artificial neural networks

Salamah¹,

Palaneeswaran²,

Savsar³

et al. 2011

Int. J. SDP

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In this paper, artifi cial neural networks (ANNs) were used to assess the performance of fl ow meters used in industrial water supply. These fl ow meters are susceptible to drift, a condition causing them to give erroneous readings that are inconsistent with the actual fl ow. A simulation of industrial water fl ow to the industrial consumers was made. This simulation contained both healthy and drifting fl ow meter readings. ANN was built and trained on the simulated data. At the time of testing, the ANN developed was correct 89.52% of the time in determining the status of the fl ow recorded by a fl ow meter. Keywords: artifi cial neural network, fl ow meter drift, industrial water supply, statistical process control. INTRODUCTIONThe importance of water cannot be overemphasized. Because of this, people realized the importance of controlling, organizing, and regulating its use. No sensible controlling, organizing, regulating, and planning can be done without accurate measurement. Hence, the abundance of fl ow meters in any water system. Accurate measurement arrangements are required for effectively managing water infrastructure regimes. Flow meters can be subject to specifi c failures such as erroneous readings and breakdown. Inaccurate measurements affect the controlling, organizing, and regulating processes of the water system and planning. Measures for avoiding and mitigating such fl ow meter problems include: calibration, repair, and replacement. Flow meter calibration generally involves checking against accurate standards to determine any appreciable deviations and correcting for errors. Yet, this calibration is not a one time transaction as it does not end the problem forever.Flow meter calibration is often neglected by the operation and maintenance staff of a water system. Drift is an error in measurement, which can increase with time. Ben Salamah et al.[1] outlined a statistical process control (SPC)-based fl ow meter drift detection method for the industrial water use regime. Realizing the usefulness of artifi cial neural network (ANN) modeling for forecasting and classifi cation objectives, an extension of this research considered specifi c applications of ANN modeling with improved SPC frameworks for fl ow meters. This paper presents some key fi ndings from the application of ANN models for detecting fl ow meter drifts.

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Application of neural‐based algorithms as statistical tools for quality control of manufacturing processes

Pacella

Semeraro

2013

Mathematical and Statistical Methods in Food Science and Technology

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A multi-layer neural network model for detecting changes in the process mean

Cited by 126 publications

References 13 publications

Artificial neural networks in variable process control: application in particleboard manufacture

Artificial neural networks in variable process control: application in particleboard manufacture

Detecting flow meter drift by using artificial neural networks

Application of neural‐based algorithms as statistical tools for quality control of manufacturing processes

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