The effect of the size of the training set and number of principal components on the false alarm rate in statistical process monitoring

Ramaker, Henk‐Jan; Sprang, E.N.M. van; Westerhuis, Johan A.; Smilde, Age K.

doi:10.1016/j.chemolab.2003.12.015

Cited by 36 publications

(19 citation statements)

References 10 publications

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“…Therefore, this distribution of scores and residuals is better comparable with those found by projection of a new independent batch. A more detailed description of this procedure and illustration of this problem can be found in R amaker et al. (2004).…”

Section: Resultsmentioning

confidence: 99%

Performance assessment and improvement of control charts for statistical batch process monitoring

Ramaker

Sprang

Westerhuis

et al. 2006

Statistica Neerlandica

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This paper describes the concepts of statistical batch process monitoring and the associated problems. It starts with an introduction to process monitoring in general which is then extended to batch process monitoring. The performance of control charts for batch process monitoring is discussed by means of two performance indices: the overall type I error and the action signal time. Problems associated with the existing approach are discussed and highlighted. Improvements are suggested and checked with the performance indices. To evaluate the effect of the proposed improvements as well as to assess the performance of the existing approach, an industrial batch production process is used.

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Section: Resultsmentioning

confidence: 99%

Performance assessment and improvement of control charts for statistical batch process monitoring

Ramaker

Sprang

Westerhuis

et al. 2006

Statistica Neerlandica

Self Cite

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“…Badcock et al 138 proposed two alternative projection techniques that focus on the temporal structure of multivariate data. Ramaker et al 139 , using simulation, studied the effect of the size of the training set and number of principal components on the false-alarm rate in statistical process monitoring.…”

Section: Pca and Autocorrelated Datamentioning

confidence: 99%

Multivariate statistical process control charts: an overview

Bersimis

Psarakis

Panaretos

2006

Quality & Reliability Eng

492

317

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In this paper we discuss the basic procedures for the implementation of multivariate statistical process control via control charting. Furthermore, we review multivariate extensions for all kinds of univariate control charts, such as multivariate Shewharttype control charts, multivariate CUSUM control charts and multivariate EWMA control charts. In addition, we review unique procedures for the construction of multivariate control charts, based on multivariate statistical techniques such as principal components analysis (PCA) and partial least squares (PLS). Finally, we describe the most significant methods for the interpretation of an out-of-control signal.

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“…The identification step consists in determining the number of PCs that have to be retained in the model. An incorrect identification of the number of significant PCs can cause further problems to the profile monitoring approach that rely on PCA results 18 . In this paper we will use a cross-validation approach to identify the significant PCs.…”

Section: Introductionmentioning

confidence: 99%

On the use of principal component analysis to identify systematic patterns in roundness profiles

Colosimo

Pacella

2007

Quality & Reliability Eng

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In many industrial applications, quality of products or processes is related to profiles. With reference to mechanical components, profiles and surfaces play a relevant role, as shown by the high number of geometric specifications characterizing most of the technical drawings. In this framework, an important step consists in identifying the systematic pattern which characterizes all the profiles machined while the process is in its standard or nominal state. With reference to this aim, this paper focuses on the use of principal component analysis (PCA) for profile data (Functional PCA). Since a usual objection to PCA is that principal components (PCs) are often difficult or impossible to interpret, this paper explores what types of profile features allow one to obtain interpretable PCs. Within the paper, a real case study related to roundness profiles of mechanical components is used as reference. In particular, functional PCA is applied to the set of real profile data to derive the significant PCs and the corresponding eigenfunctions. In order to gain insight into the information behind the retained PCs, both simulations and analytical results are used. In particular, the analytical results, outlined in the literature on functional data analysis, allow one to link the eigenfunctions to specific profile features, given that profile data admit an orthogonal basis series expansion.

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The effect of the size of the training set and number of principal components on the false alarm rate in statistical process monitoring

Cited by 36 publications

References 10 publications

Performance assessment and improvement of control charts for statistical batch process monitoring

Performance assessment and improvement of control charts for statistical batch process monitoring

Multivariate statistical process control charts: an overview

On the use of principal component analysis to identify systematic patterns in roundness profiles

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