A hierarchical process monitoring strategy for a serial multi-stage manufacturing system

Das, Anupam; Maiti, J.; Banerjee, Rohini

doi:10.1080/00207540802691382

Cited by 6 publications

(2 citation statements)

References 25 publications

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“…In case of abnormality, i.e., if any statistic breaches the control limit then the respective process and feed stock characteristics may be analysed and its contribution may be evaluated using the loadings (Choi and Lee, 2005). Furthermore the contribution of each stage may also be determined using the formulae proposed by Das et al (2010a). Thus it is evident brief from a brief case study that the multi-block PCA imparts a better understanding of the process as compared to normal PCA by providing a better insight of the process.…”

Section: Tablementioning

confidence: 99%

Monitoring of an ingot making facility via principal component analysis and its variants

Suman

Das

2019

IJPQM

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The ingots are a key intermediate product manufactured in the steel making industries. The article delves into the monitoring of the ingot manufacturing facility by making use of the multivariate process monitoring techniques. In order to deal with the various scenarios arising during the production of ingots, different variants viz. multi-block principal component analysis and multi-way principal component analysis are also employed to monitor the process and feed stock characteristics. A comparative analysis of the merits and demerits of the employed variants is also presented in the article based on the case study. On the basis of the findings it may be concluded that the decision regarding the methodology to be adopted depends upon a trade-off between the scope of application of the methodology and the ease of development.

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Section: Tablementioning

confidence: 99%

Monitoring of an ingot making facility via principal component analysis and its variants

Suman

Das

2019

IJPQM

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“…In most cases, the level of correlation is so severe that the inverse matrix solution of the data cannot be obtained. Applications of PLSR can be found in studies by Chiang and Colegrove (2007), Jarrett and Pan (2007), Stefatos and Hamza (2009), Das et al (2008Das et al ( , 2010 and Kim et al (2012), to name a few.…”

Section: Introductionmentioning

confidence: 99%

A methodology for modeling and monitoring of centrifugal casting process

Das

Mondal

Thakkar

et al. 2015

International Journal of Quality &Amp; Reliability Management

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Purpose – The purpose of this paper is to build a monitoring scheme in order to detect and subsequently eliminate abnormal behavior of the concerned casting process so as to produce worm wheels with good quality characteristics. Design/methodology/approach – In this a study, a process monitoring strategy has been devised for a centrifugal casting process using data-based multivariate statistical technique, namely, partial least squares regression (PLSR). Findings – Based on a case study, the PLSR model constructed for this study seems to mimic the actual process quite well which is evident from the various performance criteria (predicted and analysis of variance results). Practical implications – The practical implication of the study involves development of a software application with a back-end database which would be interfaced with a computer program based on PLSR algorithm for estimation of model parameters and the control limit for the monitoring chart. It would help in easy and real-time detection of faults. Originality/value – This study concerns the application of a PLSR-based monitoring strategy to a centrifugal casting process engaged in the production of worm wheel.

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Challenges and opportunities of deep learning-based process fault detection and diagnosis: a review

Zhang²

2022

Neural Comput & Applic

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A hierarchical process monitoring strategy for a serial multi-stage manufacturing system

Cited by 6 publications

References 25 publications

Monitoring of an ingot making facility via principal component analysis and its variants

Monitoring of an ingot making facility via principal component analysis and its variants

A methodology for modeling and monitoring of centrifugal casting process

Challenges and opportunities of deep learning-based process fault detection and diagnosis: a review

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