Statistical parametric and non-parametric control charts for monitoring residential water consumption

Abstract

The adoption of strategies for monitoring water consumption is essential for water resources management, contributing to the promotion of the sustainability in the water sector. Statistical process control (SPC) charts, which are widely used in the industrial sector, are statistical methods developed to improve the quality of products and processes. The application of this method has reached other areas over the last decades and has recently been identified as an option for environmental monitoring. In this context, the application of SPC charts emerges as an option for water consumption monitoring, whether in a building or an urban scale. Thus, this article aims to analyze the application of statistical process control charts in the monitoring of water consumption of two housing compounds in Joinville, southern Brazil. The methodological procedures include the use of the Shewhart and the EWMA control charts in addition to the non-parametric alternative, the EWMA-SN, assessing the effectiveness of these techniques in detecting water leaks in residential apartment buildings. The data sets, obtained through a telemetry metering system from the water utility, represent a period of 243 days. The results show that control charts are a powerful tool in identifying changes in water consumption patterns, with the EWMA chart flagging the leaks sooner.

Evaluation of Polyurethane Production Quality using Statistical Process Control Approach

“…Similarly, 35DC have the highest value in terms of the tensile strength, followed by 22DC, 18DC, 16DC and the least 14DC. In other words, the higher the density, the higher the hardness and the tensile strength (Bogo et al, 2023;Rabago, 2013) (Kaushik et al, 2015). The elongation was not consistent with the densities and this was the same for the compression factor.…”

“…Similarly, as Figure 3 illustrates, X. is 21805, the ULC is 186386, and the LCL is 13614. To put it simply, the control chart is a graphical tool used to help maintain acceptable standards of quality by conducting periodic significance tests (Bogo et al, 2023;Taylan & Darrab, 2012).…”

“…In order to understand SPC methodology, we must realize that the variations we observe in quality characteristics are caused by various factors. These factors include equipment, materials, people, methods and procedures, the environment, etc (Bogo et al, 2023). Rabago (2013) asserts that common cause variation can be quite significant.…”

“…The central line (CL) shows the characteristics of the measured mean value of the quality. Two horizontal exists and namely, upper control limit (UCL) and the lower control limits (LCL) X is the mean value of n of observation and R is the range which signifies the difference between the maximum and the minimum observation of the sample while A is the sample size (Bogo et al, 2023;Rabago, 2013) 2 (Equations 1-3).…”

An enhanced nonparametric quality control chart with application related to industrial process

“…We have also assessed the performance of the NPSR-DH chart by using the other measures of the RL such as (standard deviation of the RL (SDRL), a median of the RL (MDRL), and some percentile points of the RL ( )) because of the skewed behavior of the RL distribution (cf. Naveed et al 34 , Bataineh et al 35 and Shafqat et al 36 ). The RL measures are calculated through Monte Carlo simulations in R language.…”