Functional Regression Control Chart

Centofanti, Fabio; Lepore, Antonio; Menafoglio, Alessandra; Palumbo, Biagio; Vantini, Simone

doi:10.1080/00401706.2020.1753581

Cited by 28 publications

(28 citation statements)

References 38 publications

(53 reference statements)

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“…The control limits for both the 2 T (Equation 8) and the SPE (Equation 9) control charts are obtained as the ( ) . Other methods could be also used, either as in Centofanti et al (2020), where the distribution of the two statistics is estimated through a kernel density estimation approach, or as in Nomikos & MacGregor (1995), where a parametric approach is considered. However, all these methods are expected to provide the same results for large sample size.…”

Section: Monitoring Proceduresmentioning

confidence: 99%

“…A different research question can be for example: "given the value of the covariates, is the quality characteristic as expected? ", as faced by Capezza et al (2020) and Centofanti et al (2020).…”

Section: A Real-case Study In the Shipping Industrymentioning

confidence: 99%

“…More recently, Capezza et al (2020) extended classical multivariate techniques to the monitoring of multivariate functional data and a scalar quality characteristic related to them. Centofanti et al (2020) expand the Mandel's regression control chart (Mandel, 1969) to the functional setting, that is a control chart elaborated on the functional residuals obtained from a function-on-function regression of the quality characteristic profile on concurrent functional covariates. Other relevant contributions in this field include the work of Jin & Shi (1999), Colosimo & Pacella (2007), Colosimo & Pacella (2010), Grasso et al (2017), andBersimis et al (2018).…”

Section: Introductionmentioning

confidence: 99%

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A functional data analysis approach for the monitoring of ship CO2 emissions

et al. 2021

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Sensing networks provide nowadays massive amounts of data that in many applications provide information about curves, surfaces and vary over a continuum, usually time, and thus, can be suitably modelled as functional data. Their proper modelling by means of functional data analysis approaches naturally addresses new challenges also arising in the statistical process monitoring (SPM). Motivated by an industrial application, the objective of the present paper is to provide the reader with a very transparent set of steps for the SPM of functional data in real-world case studies: i) identifying a finite dimensional model for the functional data, based on functional principal component analysis; ii) estimating the unknown parameters; iii) designing control charts on the estimated parameters, in a nonparametric framework. The proposed SPM procedure is applied to a real-case study from the maritime field in monitoring CO2 emissions from real navigation data of a roll-on/roll-off passenger cruise ship, i.e., a ship designed to carry both passengers and wheeled vehicles that are driven on and off the ship on their own wheels. We show different scenarios highlighting clear and interpretable indications that can be extracted from the data set and support the detection of anomalous voyages.

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Section: Monitoring Proceduresmentioning

confidence: 99%

Section: A Real-case Study In the Shipping Industrymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A functional data analysis approach for the monitoring of ship CO2 emissions

et al. 2021

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“…Examples from this research line were reviewed by Megahed et al (2011). Other examples include process monitoring and quality modelling applications where data are modelled as functional data and lead to effective anomaly detection (Wang and Tsung, 2005;Menafoglio et al, 2018;Wells et al, 2013;Centofanti et al, 2021;Colosimo et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Robust Functional ANOVA with Application to Additive Manufacturing

Centofanti¹,

Colosimo²,

Grasso³

et al. 2021

Preprint

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The development of data acquisition systems is facilitating the collection of data that are apt to be modelled as functional data. In some applications, the interest lies in the identification of significant differences in group functional means defined by varying experimental conditions, which is known as functional analysis of variance (FANOVA). With real data, it is common that the sample under study is contaminated by some outliers, which can strongly bias the analysis. In this paper, we propose a new robust nonparametric functional ANOVA method (RoFANOVA) that reduces the weights of outlying functional data on the results of the analysis. It is implemented through a permutation test based on a test statistic obtained via a functional extension of the classical robust M -estimator. By means of an extensive Monte Carlo simulation study, the proposed test is compared with some alternatives already presented in the literature, in both one-way and two-way designs. The performance of the RoFANOVA is demonstrated in the framework of a motivating real-case study in the field of additive manufacturing that deals with the analysis of spatter ejections. The RoFANOVA method is implemented in the R package rofanova, available online at https://github.com/unina-sfere/rofanova.

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“…Then, Yao et al 10 presented a method based on the functional principal components decomposition 11 of both the response and covariate and Ivanescu et al 12 extended penalized functional regression (PFR) of Goldsmith et al 13 to the FoF setting. More recent articles on the FoF model are Luo and Qi 14, 15 and Centofanti et al 16, 17 …”

Section: Introductionmentioning

confidence: 99%

Function‐on‐function regression for assessing production quality in industrial manufacturing

Palumbo

Centofanti

2020

Quality & Reliability Eng

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Key responses of manufacturing processes are often represented by spatially or time‐ordered data known as functional data. In practice, these are usually treated by extracting one or few representative scalar features from them to be used in the following analysis, with the risk of discarding relevant information available in the whole profile and of drawing only partial conclusions. To avoid that, new and more sophisticated methods can be retrieved from the functional data analysis (FDA) literature. In this work, that represents a contribution in the direction of integrating FDA methods into the manufacturing field, the use of function‐on‐function linear regression modelling is proposed. The approach is based on a finite‐dimensional approximation of the regression coefficient function by means of two sets of basis functions, and two roughness penalties to control the degree of smoothness of the final estimator. The potential of the proposed method is demonstrated by applying it to a real‐life case study in powder bed fusion additive manufacturing for metals to predict the mechanical properties of an additively manufactured artefact, given the particle size distribution of the powder used for its production.

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Functional Regression Control Chart

Cited by 28 publications

References 38 publications

A functional data analysis approach for the monitoring of ship CO2 emissions

A functional data analysis approach for the monitoring of ship CO2 emissions

Robust Functional ANOVA with Application to Additive Manufacturing

Function‐on‐function regression for assessing production quality in industrial manufacturing

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