Constructing a Control Chart Using Functional Data
| Autor: | Javier Tarrío-Saavedra, Rubén Fernández-Casal, Salvador Naya, Miguel Flores, Paula Raña, Sonia Zaragoza |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
nonparametric control chart
Nonparametric control chart Computer science General Mathematics Control chart 0211 other engineering and technologies 02 engineering and technology Data depth control chart computer.software_genre 01 natural sciences 010104 statistics & probability HVAC Computer Science (miscellaneous) Critical to quality statistical process control Statistical process control 0101 mathematics Engineering (miscellaneous) energy efficiency functional data analysis 021103 operations research business.industry lcsh:Mathematics Functional data analysis Energy consumption lcsh:QA1-939 Energy efficiency Anomaly detection Data mining business data depth computer Efficient energy use |
| Zdroj: | Mathematics, Vol 8, Iss 1, p 58 (2020) RUC. Repositorio da Universidade da Coruña instname Mathematics Volume 8 Issue 1 |
| ISSN: | 2227-7390 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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