Control charts for monitoring ship operating conditions and CO<sub>2</sub> emissions based on scalar‐on‐function regression

Capezza, Christian; Lepore, Antonio; Menafoglio, Alessandra; Palumbo, Biagio; Vantini, Simone

doi:10.1002/asmb.2507

Cited by 27 publications

(29 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…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%

“…Grasso et al (2016) presented a novel approach for profile monitoring that combines the functional principal component analysis and the use of parametric warping functions. 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.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

et al. 2021

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Section: A Real-case Study In the Shipping Industrymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

et al. 2021

Self Cite

View full text Add to dashboard Cite

show abstract

“…A straightforward solution is to verify the assumption that no covariate mean shift occurs by extending to the functional setting the control charts proposed by Wade and Woodall (1993) in the multivariate case. In this regard, Capezza et al (2019) suggest to monitor the covariates X through the jointly use of the Hotelling's T 2 and SP E control charts built on the principal component decomposition of X std .…”

Section: Mean Shiftsmentioning

confidence: 99%

Functional Regression Control Chart

et al. 2020

Self Cite

View full text Add to dashboard Cite

The modern development of data acquisition technologies in many industrial processes is facilitating the collection of quality characteristics that are apt to be modelled as functions, which are usually referred to as proles. At the same time, measurements of concurrent variables, which are related to the quality characteristic proles, are often available in a functional form as well, and usually referred to as covariates. In order to adjust the monitoring of the quality characteristic proles by the eect of this additional information, a new functional control chart is elaborated on the residuals obtained from a function-on-function linear regression of the quality characteristic prole on the functional covariates. Furthermore, by means of a Monte Carlo simulation study, the performance of the proposed control chart are compared with those of other charts proposed in the literature. Eventually, a real-case study in the shipping industry is presented with the purpose of monitoring ship fuel consumption and thus, CO 2 emissions from a Ro-Pax ship, with particular regard to detecting CO 2 emission reduction after a specic energy eciency initiative.

show abstract

“…The ship CO 2 emission dataset has been thoroughly studied in the very last years [22,32,7,9]. It was provided by the shipping company Grimaldi Group to address some aspects that are related to the issue of monitoring fuel consumptions or CO 2 emissions for Ro-Pax ship that sail along a route in the Mediterranean Sea.…”

Section: Ship Co 2 Emission Datasetmentioning

confidence: 99%