Special Issue "Functional Statistics: Outliers Detection and Quality Control"

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Computational Mathematics".

Deadline for manuscript submissions: 30 April 2020.

Special Issue Editor

Prof. Javier Martínez Torres
E-Mail Website
Guest Editor
Escuela Superior de Ingeniería y Tecnología. Universidad Internacional de La Rioja. Logroño 26006, Spain
Interests: functional statistics; functional outlier; quality control; process control; capability; functional data

Special Issue Information

Dear Colleagues,

At present, a large amount of data can be approached from the functional prism and in a multitude of fields such as engineering, medicine, etc. An example of this, which is very important to the engineering (mechanical, electronic, environmental, etc.) field, is quality control, which is based on the classical Schewart methodology or WECO rules.

However, while application is important, a comparison between methods, and the design and construction of a new model, univariable or multivariable, based on depth, non-parametric, etc., is also important. Thus, in this Special Issue, different articles are collected with new models of detection of functional outliers, or applications thereof, on different areas of quality control and process capability control.

Prof. Javier Martínez Torres
Guest Editor

Manuscript Submission Information

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Mathematics is an international peer-reviewed open access monthly journal published by MDPI.

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Keywords

  • Functional outliers
  • Functional depth
  • SPC
  • Capability
  • Control process

Published Papers (2 papers)

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Research

Open AccessArticle
A Functional Data Analysis Approach for the Detection of Air Pollution Episodes and Outliers: A Case Study in Dublin, Ireland
Mathematics 2020, 8(2), 225; https://doi.org/10.3390/math8020225 - 10 Feb 2020
Abstract
Ground level concentrations of nitrogen oxide (NOx) can act as an indicator of air quality in the urban environment. In cities with relatively good air quality, and where NOx concentrations rarely exceed legal limits, adverse health effects on the population may still occur. [...] Read more.
Ground level concentrations of nitrogen oxide (NOx) can act as an indicator of air quality in the urban environment. In cities with relatively good air quality, and where NOx concentrations rarely exceed legal limits, adverse health effects on the population may still occur. Therefore, detecting small deviations in air quality and deriving methods of controlling air pollution are challenging. This study presents different data analytical methods which can be used to monitor and effectively evaluate policies or measures to reduce nitrogen oxide (NOx) emissions through the detection of pollution episodes and the removal of outliers. This method helps to identify the sources of pollution more effectively, and enhances the value of monitoring data and exceedances of limit values. It will detect outliers, changes and trend deviations in NO2 concentrations at ground level, and consists of four main steps: classical statistical description techniques, statistical process control techniques, functional analysis and a functional control process. To demonstrate the effectiveness of the outlier detection methodology proposed, it was applied to a complete one-year NO2 dataset for a sub-urban site in Dublin, Ireland in 2013. The findings demonstrate how the functional data approach improves the classical techniques for detecting outliers, and in addition, how this new methodology can facilitate a more thorough approach to defining effect air pollution control measures. Full article
(This article belongs to the Special Issue Functional Statistics: Outliers Detection and Quality Control)
Open AccessArticle
Constructing a Control Chart Using Functional Data
Mathematics 2020, 8(1), 58; https://doi.org/10.3390/math8010058 - 02 Jan 2020
Abstract
This study proposes a control chart based on functional data to detect anomalies and estimate the normal output of industrial processes and services such as those related to the energy efficiency domain. Companies providing statistical consultancy services in the fields of energy efficiency; [...] Read more.
This study proposes a control chart based on functional data to detect anomalies and estimate the normal output of industrial processes and services such as those related to the energy efficiency domain. Companies providing statistical consultancy services in the fields of energy efficiency; heating, ventilation and air conditioning (HVAC); installation and control; and big data for buildings, have been striving to solve the problem of automatic anomaly detection in buildings controlled by sensors. Given the functional nature of the critical to quality (CTQ) variables, this study proposed a new functional data analysis (FDA) control chart method based on the concept of data depth. Specifically, it developed a control methodology, including the Phase I and II control charts. It is based on the calculation of the depth of functional data, the identification of outliers by smooth bootstrap resampling and the customization of nonparametric rank control charts. A comprehensive simulation study, comprising scenarios defined with different degrees of dependence between curves, was conducted to evaluate the control procedure. The proposed statistical process control procedure was also applied to detect energy efficiency anomalies in the stores of a textile company in the Panama City. In this case, energy consumption has been defined as the CTQ variable of the HVAC system. Briefly, the proposed methodology, which combines FDA and multivariate techniques, adapts the concept of the control chart based on a specific case of functional data and thereby presents a novel alternative for controlling facilities in which the data are obtained by continuous monitoring, as is the case with a great deal of process in the framework of Industry 4.0. Full article
(This article belongs to the Special Issue Functional Statistics: Outliers Detection and Quality Control)
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