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; 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.
Highlights
Univariate and multivariate control charts are applied to identify anomalies in the industry and control the quality of products and services
Diverse solutions have been provided that propose the modification and application of exponentially weighted moving average chart (EWMA) control charts [1]; control charts based on the fitting of autoregressive integrated moving average (ARIMA) models [2]; and the use of control charts for profiles, which is understood as the control of the parameters that define the relationship between two different critical to quality (CTQ) variables [3,4,5,6]
The present study proposes methodologies to build control charts that allow us to control the aforementioned processes. in order to test the applied statistical methodologies for anomaly detection properly, these companies have provided a database on a real HVAC installation, whose anomalies and assignable causes are identified by its maintenance personnel
Summary
Univariate and multivariate control charts are applied to identify anomalies in the industry and control the quality of products and services. This is the case of data in the fields of energy consumption, indoor and machine temperatures, relative humidity, amount of CO2 , among other variables, measured in all types of buildings These new data, which currently characterize industrial processes, require innovative solutions, developed by researchers in the field of statistical quality control, based on the application of functional data analysis (FDA) techniques. Fridama’s facilities, Σqus (web platforms for big data management) and Nerxus (statistical consultancy for data analysis in the energy sector); the solution to the problem raised by this company is defined by continuously monitored data over time that can be treated as profiles or functional data Based on this need, the present study proposes methodologies to build control charts that allow us to control the aforementioned processes. The use of FDA techniques allows us to consider the autocorrelation of the data as well as, by means of resampling techniques, to circumvent parametric assumptions about the trend and dependence
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
