On Model Selection for Autocorrelated Processes in Statistical Process Control

Abstract

Using traditional control charts to monitor autocorrelated processes is not beneficial, because it will lead us to misleading detections in the processes. One of the methods used to deal with the control charts for autocorrelated process is the model-based approach. It uses an adequate time series model that fits the process and uses the residuals as monitoring statistics. For the said purpose, it is important to pick a suitable model that can adequately be used for different designs of control charts under specific time series model. This study intends to do the same for three popular types of charts namely Shewhart, exponentially weighted moving average, and cumulative sum. The models covered in this study include AR(1), MA (1), and ARMA(1,1) as the potential models to fit the process of interest. We have focused on two performance aspects namely efficiency and robustness. Average run length is used as a performance measure for different in-control and outof-control states of the autocorrelated processes under varying levels of autocorrelation. An application example based on a real data set is also included in the study to highlight the importance of the study proposals.

Review of Underground Storage Tank Condition Monitoring Techniques

“…On the contrary, a plain, direct model could face a bottleneck when analysing complicated situation such as simultaneous monitoring and weighting of abundant signals. Comparatively, outlier detection can be achieved by extracting fundamental vibration features and applying it to techniques such as statistical process control (SPC) [78], adaptive template matching (ATM) [79], wavelet transform (WT) [80], or Fourier Transform [81], but the aforementioned methods are far away from mimicking reality and demand human supervision.…”

Simulation approach for assessing the performance of the γEWMA control chart

“…Most common EWMA control charts assume normality, which is reflected in publications such as Crowder (1987), Lucas and Saccucci (1990), Jones et al (2001), Koehle et al (2001), Khoo et al (2015), Dawod et al (2017), andSupharakonsakun, et al (2019). A EWMA control charts propriety analyzed the accumulation of previous and current information then the past is hefted, and history is measured in the hope that it is predictive.…”

“…They presented a view broadly and compendious summary of the work on the development of the control charts for variables to monitor processes for autocorrelated data. Harris and Ross (1991), Koehle et al (2001), and Dawod et al (2017) analyzed the effect of autocorrelation on ARL by considering a shift in the process mean; however, they did not evaluate the effect of the smoothing parameter or the effects of the distribution observations. Ulkhaq and Dewanta (2017) and Supharakonsakun et al (2019) considered the effect of the smoothing parameter but only for normal observations.…”

“…In referring to control chart performance, in the literature, it has been evaluated using indicators that were derived from the Run-Length (RL). These indicators include the Average-Run-Length (ARL), the Standard-Deviation-Run-Length (SDRL), and the Median-Run-Length (MRL) (B. C. Khoo et al, 2015;Borror et al, 1999;Dawod et al, 2017;Esparza Albarracin et al, 2018;Human et al, 2011;Jones et al, 2001). For instance, it is desirable for the ARL to be large if no assignable cause has occurred, and small if one out-of-control condition has occurred.…”

Perbandingan Bagan Kendali Modifikasi Shewhart dan Bagan Kendali ARMAST pada ARMA(1,1)

“…Semakin kecil ARL, semakin cepat grafik kendali mendeteksi adanya pergeseran.Jenis data yang digunakan terdiri atas dua data yaitu data simulasi dan data asli. Data simulasi yaitu berupa data yang dibangkitkan dengan menggunakan program R 3 4…”