Cumulative Conforming Control Chart Assuming Discrete Weibull Distribution

Ali, Sajid; Zafar, Tanzila; Shah, Ismail; Wang, Lichen

doi:10.1109/access.2020.2964602

Cited by 9 publications

(6 citation statements)

References 35 publications

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“…Others [8] have studied time-truncated charts for exponentiated half logistic distribution and Dagum distribution, respectively. Further, [9][10][11][12] show further control chart applications for various statistical distributions.…”

Section: Introductionmentioning

confidence: 89%

Performance of a New Time-Truncated Control Chart for Weibull Distribution Under Uncertainty

AL-Marshadi¹,

Shafqat²,

Aslam³

et al. 2021

IJCIS

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To detect indeterminacy effect in the manufacturing process, attribute control chart using neutrosophic Weibull distribution is proposed in this paper. To make the attribute control chart more efficient for persistent shifts in the industrial process, an attribute control chart using Weibull distribution has been proposed recently. In this study, a neutrosophic Weibull distribution-based attribute control chart develop for efficient monitoring of the process. The indeterminacy effect was studied with the control chart's performance using characteristics of run length. In addition, the proposed chart effectively detected shifts in uncertainty. The relative efficiency of the proposed structure is compared with the existing attribute control chart under the Weibull timetruncated life test. The relative analysis reveals that the proposed time-truncated control chart for Weibull distribution under uncertainty design performance more efficiently than the existing counterparts. From the comparison, the proposed chart provides smaller values for the out-of-control average run length as compared to the existing attribute control chart. An illustrative application related to automobile manufacturing is also incorporated to demonstrate the proposal.

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Section: Introductionmentioning

confidence: 89%

Performance of a New Time-Truncated Control Chart for Weibull Distribution Under Uncertainty

AL-Marshadi¹,

Shafqat²,

Aslam³

et al. 2021

IJCIS

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“…If the computed CVRL value is greater than the threshold value, the production process is out-of-control, and corrective action may be necessary to bring it back into control. The CVRL values are categorized as in-control CVRL (CVRL 0 ) and out-of-control CVRL (CVRL 1 ) 48 – 50 .…”

Section: Performance Measuresmentioning

confidence: 99%

Performance evaluation of DEWMA3 in phase-II for capturing changes in simple linear profiles based on run rule mechanism

Sherwani

Qasim

Abbas

et al. 2023

Sci Rep

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In Statistical Process Control, many techniques exist for monitoring the stability of a process over time. In this work, we study the relationship of the response variable with explanatory variables in the form of linear profiles for detecting changes in slope and intercept of the linear quality profiles. We used the transformation of explanatory variables approach used for make the regression estimates independent of each other to have zero average. A comparative study of three phase-II methods using DEWMA statistics in monitoring and capturing undesirable deviations in the slope, intercept, and variability is also studied by applying different proposed run rules schemes i.e., R1/1, R2/3, R3/3. Monte Carlo simulations were carried out on R-Software for finding the results of proposed schemes by taking various levels of shifts for intercept, slope, and standard deviation in identifying the false alarm rate of a process. The simulation results based on the average run length criterion show that the proposed run rule schemes improve the detection ability of the control structure. Among all the proposed schemes R2/3 is found to be the best one because of its quick detection ability of false alarm rate. The proposed scheme also shows superiority in comparison to other schemes. The simulation results are further justified with a real data application.

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“…The pmf of the random variable Y thus defined may be viewed as discrete concentration (Roy (2003) [14] ) of the pdf of X. Using this concept, a two-parameter discrete probability distribution is proposed by discretizing the re-parameterized version of the twoparameter ETE ( , ) given in (1). First re-parameterization of Erlang truncated-exponential distribution in ( 1) is done by taking = −(1− − ) and , this lead us to the formula of the pmf of discrete Erlang-Truncated Exponential distribution (DETE), as follows:…”

Section: Discrete Erlang-truncated Exponential Distributionmentioning

confidence: 99%

“…Jayakumar and Babu (2019) [6] introduced a discrete version of the additive Weibull geometric distribution. Ali et al (2020) [1] introduced a new discrete Time Between Events control chart following discrete Weibull distribution, by derived the design of the proposed chart analytically and discussed numerically. Elbatal and Aldukeel (2021) [2] discussed the McDonald Erlang-truncated exponential distribution with three shape parameters.…”

Section: Introductionmentioning

confidence: 99%

Discrete Erlang-truncated exponential distributions

El-Alosey¹

2021

Int. J. Stat. Appl. Math.

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In this paper, the discrete Erlang-truncated exponential distribution is defined by using the general approach of discretizing a continuous distribution while retaining its survival function. The statistical properties of the discrete Erlang-truncated exponential distribution such as the quantile function, moments, moment generating function, Rényi entropy and order statistics are calculated. The estimation of the parameters of the model is approached by the maximum likelihood (ML) method. The stressstrength parameter is obtained and estimated by using ML method.

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Cumulative Conforming Control Chart Assuming Discrete Weibull Distribution

Cited by 9 publications

References 35 publications

Performance of a New Time-Truncated Control Chart for Weibull Distribution Under Uncertainty

Performance of a New Time-Truncated Control Chart for Weibull Distribution Under Uncertainty

Performance evaluation of DEWMA3 in phase-II for capturing changes in simple linear profiles based on run rule mechanism

Discrete Erlang-truncated exponential distributions

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