A Statistical Control Chart for Stationary Process Data

Zhang, Nien Fan

doi:10.1080/00401706.1998.10485479

Cited by 117 publications

(88 citation statements)

References 20 publications

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“…In order to evaluate the effectiveness, the monitoring results of the proposed method are compared with other wellknown control charts, namely, SCC (Wardell et al 1994), EWMAST (Zhang 1998) and ARMAST (Jiang et al 2000). The comparisons of ARL 1 values for the proposed method and SCC, EWMAST, ARMAST charts applied to the AR(1) process data with various levels of autocorrelation and stepchange disturbance are demonstrated in Table 4.…”

Section: Resultsmentioning

confidence: 99%

“…If the autocorrelated characteristic of the monitoring process is changed, we need to rebuild the model. Finally, the potential mixed effects of modeling errors and process mean changes may render the developed control chart useless or misleading (Hwarng 2004;Lu and Reynolds 1999;Zhang 1998). Therefore, the performance of the time-series based control charts depends highly on the adequacy of the fitted model.…”

Section: No Training Phasementioning

confidence: 97%

“…However, the process datasets collected from most continuous and batch process operations are usually serially correlated (Alwan and Roberts 1988;Chiu et al 2001Chiu et al , 2003Zhang 1998). It has been found that the performance of traditional control charts is significantly affected by autocorrelated data (Altiok and Melamed 2001;Alwan 1992;Harris and Ross 1991;Woodall and Faltin 1993;Zhang 1998). When traditional SPC techniques of control charts are applied for monitoring autocorrelated process data, the most important effect of autocorrelation is in the generation of a large number of false out-of-control signals.…”

Section: Introductionmentioning

confidence: 97%

“…The basic assumption of SPC charts is that the processing datasets are independently and identically distributed (IID) about the process mean (Montgomery 2001). However, the process datasets collected from most continuous and batch process operations are usually serially correlated (Alwan and Roberts 1988;Chiu et al 2001Chiu et al , 2003Zhang 1998). It has been found that the performance of traditional control charts is significantly affected by autocorrelated data (Altiok and Melamed 2001;Alwan 1992;Harris and Ross 1991;Woodall and Faltin 1993;Zhang 1998).…”

Section: Introductionmentioning

confidence: 97%

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An independent component analysis-based disturbance separation scheme for statistical process monitoring

2010

J Intell Manuf

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In this paper, an independent component analysis (ICA)-based disturbance separation scheme is proposed for statistical process monitoring. ICA is a novel statistical signal processing technique and has been widely applied in medical signal processing, audio signal processing, feature extraction and face recognition. However, there are still few applications of using ICA in process monitoring. In the proposed scheme, ICA is first applied to in-control training process data to determine the de-mixing matrix and the corresponding independent components (ICs). The IC representing the white noise information of the training data is then identified and the associated row vector of the IC in the de-mixing matrix is preserved. The preserved row vector is then used to generate the monitoring IC of the process data under monitoring. The disturbances in the monitoring process can be effectively enhanced in the monitoring IC. Finally, the traditional exponentially weighted moving average control chart is used to the monitoring IC for process control. For evaluating the effectiveness of the proposed scheme, simulated manufacturing process datasets with step-change disturbance are evaluated. Experiments reveal that the proposed monitoring scheme outperforms the traditional control charts in most instances and thus is effective for statistical process monitoring.

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Section: Resultsmentioning

confidence: 99%

Section: No Training Phasementioning

confidence: 97%

Section: Introductionmentioning

confidence: 97%

Section: Introductionmentioning

confidence: 97%

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An independent component analysis-based disturbance separation scheme for statistical process monitoring

2010

J Intell Manuf

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“…Además, la gestión de recursos se debe realizar con el fin de asegurar la disponibilidad de recursos e información necesarios para apoyar la operación y el seguimiento de estos procesos en pro de la gestión convenientemente de los productos no conformes, es decir, realizar el seguimiento y la medición cuando sea aplicable, para lo cual se pueden aplicar las siete herramientas de la calidad, estas comprenden: diagramas de Ishikawa que permiten determinar causas de los eventos de no calidad, hojas de registro para contabilizar la frecuencia de ocurrencia, los gráficos o cartas de control para verificar la estabilidad de los procesos, a la utilización de esta herramienta, [35] hace una contextualización para ajustar las cartas de control de acuerdo a las características de los procesos a partir de la perspectiva de autores como [36][37][38][39], quienes concuerdan en afirmar que los procesos no son tan ideales como lo muestra Shewhart ya que los métodos tradicionales de control estadístico pierden efectividad cuando se realizan pruebas con datos autocorrelacionados, en este trabajo se evidencia la aplicación de técnicas de muestreo, distribuciones de probabilidad y modelos de pronóstico para parametrizar procesos.…”

Section: Resultsunclassified

Sistema de gestión ISO 9001-2015: Técnicas y herramientas de ingenieria de calidad para su implementación

Medina

Díaz

Cárdenas

2017

I2+D

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ResumenEl presente artículo muestra una evolución de la calidad en función de la normatividad existente en busca de mejora de procesos, además, como las normas ISO 9000 impactan en el mejoramiento continuo de las organizaciones optimizando los productos ofrecidos al consumidor final. De igual forma el análisis de como la ingeniería de calidad realiza los aportes necesarios en la generación de técnicas y métodos de implementación para seguimiento y control de procesos basados en estadísticas y modelos matemáticos útiles para reducción de costos, reducción de tiempos y mejora de calidad de vida de los empleados, finalmente se realiza un interrelacionar de los requisitos de los que trata la norma ISO 9001:2015 y las herramientas que desde la perspectiva de la ingeniería de la Calidad se pudieran aplicar a fin de garantizar el éxito y sostenibilidad de un Sistema de Gestión Calidad en una organización ya sea de bienes o servicios.Palabras clave: ingeniería de calidad, sistema de gestión de la calidad, control estadístico, mejora de la calidad. AbstractThis article shows an evolution of quality management system according to the existing regulations in search process improvement, as well as the ISO 9000 standards impact on continuous improvement of organizations by optimizing the products offered to the final consumer. Similarly analysis and quality engineering makes the necessary inputs in the generation of technical and implementation methods for monitoring and control based on statistics and mathematical models useful for cost reduction, reduction of time and quality improvement processes lives of employees, finally a interrelate with the requirements of ISO 9001:2015 that is standard and tools from the perspective of quality engineering could be applied to ensure the success and sustainability of a system is carried out Quality Management in an organization either goods or services.Key words: Quality Engineering, Management Systems Quality, statistical Control, quality improvement. INTRODUCCIÓNEl concepto de la calidad se manifiesta desde la antigüedad, por ejemplo en la construcción de las pirámides de Egipto, en donde la calidad se veía reflejada en la perfección con la que cada piedra fue cortada y encajaba con las otras, hasta formar la compleja estructura piramidal. En la época artesanal, la calidad se concibe como el cumplimiento de los requerimientos establecidos para la personalización de los productos que son manufacturados por expertos maestros. Luego, en la primera mitad del siglo XX con la aparición de las teorías

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Autocorrelated Data

Wardell¹

2007

Encyclopedia of Statistics in Quality and Reliability

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Traditional statistical process control methods assume that consecutive observations on a control chart are statistically independent. Often observations from actual processes are serially correlated. In such cases, the use of traditional control charts often leads to misleading conclusions as to whether or not the process is under control. We illustrate the problems of using traditional control charts and review several methods that have been proposed to handle the autocorrelation. Some of the methods are model based while others are model free. The majority of the methods are improvements over traditional control charts, but none is able to completely overcome the inherent difficulty of monitoring autocorrelated data. Hence there are some cases where traditional charts, which are simpler to implement, should be considered even when the process is autocorrelated.

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A Statistical Control Chart for Stationary Process Data

Cited by 117 publications

References 20 publications

An independent component analysis-based disturbance separation scheme for statistical process monitoring

An independent component analysis-based disturbance separation scheme for statistical process monitoring

Sistema de gestión ISO 9001-2015: Técnicas y herramientas de ingenieria de calidad para su implementación

Autocorrelated Data

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