The exact statistical properties of a signed‐rank‐based adaptive exponential weighted moving average chart

For effective monitoring of the range of sustainable changes in the process median, a nonparametric adaptive cumulative sum signed‐rank (NPACUSUM‐SR) control chart has been suggested in this study. Through the use of Monte Carlo simulation, the numerical analysis of the suggested NPACUSUM‐SR scheme has been calculated for zero and steady states. Run length (RL) profiles have been used to explore and compare the proposed NPACUSUM‐SR chart's in‐control resilience under various symmetrical heavy‐tailed, light‐tailed, and contaminated normal distributions. The suggested NPACUSUM‐SR chart has undergone a thorough investigation of the competition using Wilcoxon signed‐rank statistics and sign statistics under steady‐state and zero‐state conditions. The comparison shows that the suggested NPACUSUM‐SR design is superior to its competitors, especially when considering steady‐state performance over a wide range of shifts. This study examines the viability of the suggested design through a manufacturing‐related application.

“…Tang et al 59,60 . suggested NP AEWMA sign (NPAEWMA‐SN) and NP AEWMA signed‐rank (NPAEWMA‐SR) charts for monitoring the process median.…”

Section: Relative Comparisonmentioning

confidence: 99%

“…Tang et al 59,60 suggested NP AEWMA sign (NPAEWMA-SN) and NP AEWMA signed-rank (NPAEWMA-SR) charts for monitoring the process median. The comparison between the proposed and NP adaptive charting schemes at various design parameters has been reported in Figure 5.…”

Section: Proposed Versus Nonparametric Adaptive Charting Schemesmentioning

confidence: 99%

Nonparametric adaptive cumulative sum charting scheme for monitoring process location

Abbas,

Nazir,

Xiang

et al. 2024

“…A lot of work has been done on optimal design of control charts in literature [e.g. Capizzi and Masarotto, 34 , Castagliola et al., 35 Khoo et al., 36 Castagliola et al., 37 Saha et al., 38 Yeong et al., 39 Chong et al., 40 Tang et al., 41 Xie et al., 42 Yeong et al 43 …”

Section: Optimal Choice For the Parameters Of The Individual Two‐para...mentioning

confidence: 99%

“…A lot of work has been done on optimal design of control charts in literature [e.g. Capizzi and Masarotto, 34 , Castagliola et al, 35 Khoo et al, 36 Castagliola et al, 37 Saha et al, 38 Yeong et al, 39 Chong et al, 40 Tang et al, 41 Xie et al, 42 Yeong et al 43 ] based on minimizing the out-of-control value of various performance criteria. Since all the study here has been based on ARL (which is the most commonly used performance criterion) the optimal design of the EWMA control chart will be done by minimizing the ARL.…”

Section: Optimal Choice For the Parameters Of The Individual Two-para...mentioning

confidence: 99%

Probability‐type, Shewhart‐type and EWMA control charts for individual observations from the two‐parameter Lindley distribution

Demertzi,

Psarakis

2023

Lindley distribution is a continuous distribution with various applications some of which are in medicine, genetics, epidemiology, biology, finance and actuarial sciences, ecology, meteorology, sociology, demography, agriculture, hydrology, geosciences, reliability and engineering, life testing and survival analysis, airborne systems and communications, environmental studies and modeling and describing of human mistakes, strikes, accidents, behavioural and emotional or IQ test scores and waiting times of customers in queues until service, etc. Due to its variety of applications, it appears to be important that control charts for detected shifts in a process should be constructed under the assumption that the quality characteristic of interest follows a Lindley‐related distribution. Here, we construct probability‐type, Shewhart‐type and EWMA control charts (and deal with the optimal choice of its parameters) for individual observations from the two‐parameter Lindley distribution, investigate and compare their performance and illustrate them using examples with both simulated and real data. The whole analysis reveals the superiority of using skewness correction for the construction of the control charts against not using it.

Nonparametric Hampel Score‐Function‐Based Adaptive EWMA Charts for Monitoring Location of Manufacturing Industrial Process

Abbas,

Nazir,

Fang

et al. 2024

Control charts are applied to monitor changes in the process parameter(s). In the manufacturing industry, shifts in the process parameter(s) are generally unknown so the conventional monitoring techniques only perform well for the predefined shifts. To tackle the unknown range of shifts in the process parameter(s), adaptive charting techniques are preferred. The Hampel score‐function‐based adaptive exponentially weighted moving average ( chart has been recently investigated under the assumption of the normality of the process. However, in industrial processes, there is a lack of knowledge about the process distribution; in such cases, nonparametric charts are the better choice for practitioners. This study proposes nonparametric Hampel function‐based adaptive exponentially weighted moving average sign (NPHAEWMA‐SN) and Wilcoxon signed‐rank (NPHAEWMA‐SR) charts as alternatives to the chart for monitoring unknown changes in the location of the process. The proposed charts (NPHAEWMA‐SN and NPHAEWMA‐SR) proved robust and efficient alternatives to the counterparts against the symmetrical heavy‐tailed, extreme value, and contaminated normal distributions at certain and over the range of shifts. Three artificial datasets have been taken from different distributions to verify the robustness and detection ability of the proposals. An industrial dataset has also been taken from the piston rings manufacturing industry for the application of the proposals.