An efficient approach of designing distribution-free exponentially weighted moving average schemes with dynamic fast initial response for joint monitoring of location and scale

Song, Zhi; Mukherjee, Amitava; Zhang, Jiujun

doi:10.1080/00949655.2020.1776711

Cited by 17 publications

(7 citation statements)

References 40 publications

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“…From the results, about 43 papers have considered the normal distribution, while 10 and 6 involve distribution‐free and non‐normal quality characteristics, respectively. Chowdhury et al., 48 Chowdhury et al., 52 Chong et al., 63 Zhang et al., 67 Mahmood et al., 69 Chong et al., 72 Huang et al., 23 Song et al., 77 Chong et al., 32 and Zhang et al 36 . have published papers in the field of joint monitoring using distribution‐free schemes.…”

Section: Analytic Resultsmentioning

confidence: 99%

“…From the results, about 43 papers have considered the normal distribution, while 10 and 6 involve distribution-free and non-normal quality characteristics, respectively. Chowdhury et al, 48 Chowdhury et al, 52 Chong et al, 63 Zhang et al, 67 Mahmood et al, 69 Chong et al, 72 Huang et al, 23 Song et al, 77 Chong et al, 32 and Zhang et al 36 have published papers in the field of joint monitoring using distribution-free schemes. Mukherjee et al, 53 Celano et al, 58 Mukherjee, 65 Wang et al, 21 Sanusi and Mukherjee, 27 and Sanusi et al 28 considered non-normal distributions for the monitored quality characteristics.…”

Section: Distribution Of Quality Characteristicsmentioning

confidence: 99%

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A literature review on joint control schemes in statistical process monitoring

Jalilibal

Amiri

Khoo

2022

Quality & Reliability Eng

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Process monitoring is regarded as a continuous phenomenon requiring careful consideration to acquire an enhanced output quality. Dispersion and location are significant parameters in the entire process, and timely detection of the changes that occur in a stable process is needed. Today, quality practitioners recommend using a single charting setup offering better capability of detecting joint changes in the parameters of a process. As provided in the literature, a detailed review paper for simultaneous monitoring is conducted in 2013 which many reaserchers were attracted to publish papers in this field. In this paper, a meticulous content analysis (on the basis of 59 reviewed papers in the field of joint monitoring from 2013 to 2021) is exploited to classify the papers that includes joint control charts for statistical process monitoring (SPM), to identify the potential topics and present some suggestions for further studies in simultaneous monitoring.

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Section: Analytic Resultsmentioning

confidence: 99%

Section: Distribution Of Quality Characteristicsmentioning

confidence: 99%

A literature review on joint control schemes in statistical process monitoring

Jalilibal

Amiri

Khoo

2022

Quality & Reliability Eng

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“…where the initial values are set to be equal to 1. The monitoring statistic , EWMA j R of the proposed scheme is a max-type combination of the above-mentioned statistics, namely Song et al (2020a) introduced the concept of an EWMA monitoring scheme with a dynamic fast initial feature (FIR, hereafter) that restricts initial false alarms and also improves the out-of-control attribution of the resulting framework. More precisely, they established distributionfree FIR-based EWMA monitoring frameworks which rely on either the Lepage or the Cucconi statistic.…”

Section: Nonparametric Ewma Control Schemes Based On Lepage-type Statisticsmentioning

confidence: 99%

Nonparametric EWMA-Type Control Charts for Monitoring Industrial Processes: An Overview

Triantafyllou¹,

Ram²

2021

Int J Math, Eng, Manag Sci

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In the present paper we provide an up-to-date overview of nonparametric Exponentially Weighted Moving Average (EWMA) control charts. Due to their nonparametric nature, such memory-type schemes are proved to be very useful for monitoring industrial processes, where the output cannot match to a particular probability distribution. Several fundamental contributions on the topic are mentioned, while recent advances are also presented in some detail. In addition, some practical applications of the nonparametric EWMA-type control charts are highlighted, in order to emphasize their crucial role in the contemporary online statistical process control.

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“…e EDC chart provided a good overall detection over the DC and dual-Crosier CUSUM (DCC) charts when detecting a range of mean shift sizes. After that, Song et al [26] presented an efficient approach of designing distribution-free exponentially weighted moving average schemes with dynamic fast initial response for joint monitoring of location and scale. Furthermore, Saha et al [27] advocated two CUSUM schemes for simultaneous monitoring of unknown parameters of a shifted exponential process and its application in monitoring of call durations in telemarketing.…”

Section: Introductionmentioning

confidence: 99%

Novel Mixed EWMA Dual-Crosier CUSUM Mean Charts without and with Auxiliary Information

Arslan

Ashraf

Anwar

et al. 2022

Mathematical Problems in Engineering

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The classical cumulative SUM (CUSUM) chart is commonly used to monitor a particular size of the mean shift. In many real processes, it is assumed that the shift level varies within a range, and the exact level of the shift size is mostly unknown. For detecting a range of shift size, the dual-CUSUM (DC) and dual-Crosier CUSUM (DCC) charts are used to provide better detection ability as compared to the CUSUM and Crosier CUSUM (CC) charts, respectively. This paper introduces a new mixed exponentially weighted moving average (EWMA)-DCC (EDCC) chart to monitor process mean. In addition, AIB-based EWMA-DC (EDC) and EDCC charts (namely, AIB-EDC and AIB-EDCC charts) are suggested to detect shifts in the process mean level. Monte Carlo simulations are used to compute the run length (RL) characteristics of the proposed charts. A detailed comparison of the proposed schemes with other competing charts is also provided. It turns out that the proposed chart provides better performance than the counterparts when detecting a range of mean shift sizes. A real-life application is also presented to illustrate the implementation of the existing and proposed charts.

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An efficient approach of designing distribution-free exponentially weighted moving average schemes with dynamic fast initial response for joint monitoring of location and scale

Cited by 17 publications

References 40 publications

A literature review on joint control schemes in statistical process monitoring

A literature review on joint control schemes in statistical process monitoring

Nonparametric EWMA-Type Control Charts for Monitoring Industrial Processes: An Overview

Novel Mixed EWMA Dual-Crosier CUSUM Mean Charts without and with Auxiliary Information

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