Enhanced quality control in continuous flow processes

English, John R.; Sastri, Tep

doi:10.1016/0360-8352(90)90117-5

Cited by 18 publications

(7 citation statements)

References 3 publications

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“…In the SPC area, Crowder (1986) studied the Kalman filter SPC for geometric moving average processes. English et al (1989English et al ( , 1991, and English & Sastri (1990) suggested using the Kalman filter to monitor autoregressive processes. These studies are all for univariate rather than multivariate time series.…”

Section: Vector State Space Chartmentioning

confidence: 99%

Applying State Space to SPC: Monitoring Multivariate Time Series

Pan

Jarrett

2004

Journal of Applied Statistics

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Monitoring cross-sectional and serially interdependent processes has become a new issue in statistical process control (SPC). In up-to-date SPC literature, Kalman filtering was reported to monitor univariate autocorrelated processes. This paper applies a Kalman filter or state-space method for SPC to monitoring multivariate time series. We use Aoki's approach to estimate the parameter matrices of a state-space model. Multivariate Hotelling T2 control charts are employed to monitor the residuals of the state-space. Examples of this approach are illustrated.Quality Control Charts, Spc, State-space, Multivariate Time Series, Aoki's Approach,

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Section: Vector State Space Chartmentioning

confidence: 99%

Applying State Space to SPC: Monitoring Multivariate Time Series

Pan

Jarrett

2004

Journal of Applied Statistics

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“…Bensingh, et al (2019) implemented hybrid ANN model and parti-and to detect the engine problems [50]. English and Sastri (1990) explained a new technique regarding quality control in a steady-state manufacturing process using ANN technique and a close correlation was found between ANN model prediction results and real life results [51]. Patrick (1991) presented an application of ANN on washing operations.…”

Section: Manufacturing Operationsmentioning

confidence: 99%

The Potential Applications of Machine Learning-Based Artificial Neural Networking Algorithms: A Review

Channa¹,

Arif²

2021

Int J Signal Process Anal

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Machine learning based artificial neural networking (ANN) has been acknowledged to be an authentic way to accomplish intricate pattern identification and regression analysis without any obvious necessity to paradigm and resolve the primary physical models. ANN has been introduced and adopted in various fields of life based-on their key advantages including learning and adapting ability, parallel distributed computation, robustness, and many more. This review articles discussesthe working principle, classes and structure of ANN. The potential applicable areas of ANN, the challenges and their solutions that have been identified through literature survey, are discussed and summarized. KeywordsMachine learning, Artificial neural networking (ANN), Potential applications of ANN, Challenges and solutions in ANN chine learning algorithms to detect anomalies correlations while performing test searching for patterns across the various data feeds, so facilitating the manufacturing process and making it easier for people to work [1].Although, there are tremendous applications of AI in various aspects of manufacturing systems but expert systems are the most commonly used application of AI. Expert systems are commonly used in designing, scheduling, process planning,

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“…Alwan et al [13] proposed a general approach to monitor residuals of Univariate auto correlated time series where the systematic patterns are filtered out and the special changes are more exposed. Other studies include Montgomery and Friedman [46], Harris, et al [47], Montgomery, et al [12], Maragah, et al [48], Wardell, et al [49], Lu, et al [15], West, et al [22] and West, et al [50], English, et al [51], Pan, et al [52] suggested state space methodology for the control of auto correlated process. Further, additional technologies implemented by Testik [53], Yang, et al [54] and Yeh, et al [9] provide newer methods for enabling better MPC methods.…”

Section: Interpretation Of Multivariate Process Controlmentioning

confidence: 99%

Improving Detection in Managing Health and Medical Care with Data Analytics

Je¹

2015

Bus Eco J

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The purpose is to introduce the demand for the quality movement practice in problems associated with public health. We show both the need and application of quality monitoring, especially the need for multivariate quality concepts to reduce the costs of operating public health programs to control the flow of problems in the dynamic behavior of these systems in public health systems such as water treatment to utilize concepts associated with multivariate methods and auto correlated time series. One of the most difficult problems involves the use of multivariate quality control in the scientific and health environment. Multiple diagnoses require use of advanced techniques to arrive at complex solutions to medical and health problems. We explore these problems. Business and Economics Journal B us ines s a n d E cono m ic s Jo urna l

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Enhanced quality control in continuous flow processes

Cited by 18 publications

References 3 publications

Applying State Space to SPC: Monitoring Multivariate Time Series

Applying State Space to SPC: Monitoring Multivariate Time Series

The Potential Applications of Machine Learning-Based Artificial Neural Networking Algorithms: A Review

Improving Detection in Managing Health and Medical Care with Data Analytics

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