A nonparametric adaptive EWMA control chart for monitoring multivariate time-between-events-and-amplitude data

Xue, Li; An, Lisheng; Feng, Sen; Liu, Yumin; Wu, Haochen; Wang, Qiuyu

doi:10.1016/j.cie.2024.110250

Cited by 5 publications

(1 citation statement)

References 41 publications

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“…For instance, Riaz et al introduced a sensitive non-parametric EWMA scheme in 2015 and a non-parametric double EWMA scheme in 2016 for an improved monitoring of process location [23,24]. Xue et al [25] designed a non-parametric adaptive EWMA scheme for monitoring multivariate time-between-events-and-amplitude data.…”

Section: Introductionmentioning

confidence: 99%

Weighted-Likelihood-Ratio-Based EWMA Schemes for Monitoring Geometric Distributions

Zhang,

Cai,

Zhang

2024

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Monitoring the parameter of discrete distributions is common in industrial production. Also, it is often crucial to monitor the parameter of geometric distribution, which is often regarded as the nonconforming item rate. To enhance the detection of nonconforming item, we designed an exponentially weighted moving average (EWMA) scheme based on the weighted likelihood ratio test (WLRT) method, and this scheme is denoted as the EWLRT scheme, specifically designed for monitoring the increase of the parameter in geometric distribution. Moreover, the optimal statistical design of the EWLRT scheme is presented when the shift is known. Results from numerical comparisons through Monte Carlo simulations indicates that the EWLRT scheme performs better than the competing schemes in some scenarios. Additionally, the designed scheme is characterized by its simplicity and ease of use, making it ideally suited for scenarios involving single observation. An example is illustrated to demonstrate the effectiveness of the EWLRT scheme.

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Section: Introductionmentioning

confidence: 99%