Applying SVSSI sampling scheme on np-chart to Decrease the Time of Detecting Shifts - Markov chain approach, and Monte Carlo simulation

Shojaee, Mohsen; Namin, Samrad Jafarian; Ghomi, S. M. T. Fatemi; Imani, Din Mohammad; Faraz, Alireza; Nezhad, Mohammad Saber Fallah

doi:10.24200/sci.2020.52677.2833

Cited by 4 publications

(3 citation statements)

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“…As future research, it is suggested to investigate other monitoring charts reviewed by Thaga and Sivasamy [9] for similar models. Designing attribute control charts with different sampling schemes, similar to [59], can be extended under the integrated model. Uncertainty in the model can be treated using a robust optimization approach applied in [54] and [60].…”

Section: Discussionmentioning

confidence: 99%

An integrated model for optimal selection of quality, maintenance, and production parameters with auto correlated data

et al. 2021

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Statistical process monitoring, maintenance policy, and production have commonly been studied separately in the literature, whereas their integration can lead to more favorable conditions for the entire production system. Among all studies on integrated models, the underlying process is assumed to generate independent data. However, there are practical examples in which this assumption is violated because of the extraction of correlation patterns. Autocorrelation causes numerous false alarms when the process is in the in-control state or makes the traditional control charts react slowly to the detection of an out-of-control state. The autoregressive moving average (ARMA) control chart is selected as an effective tool for monitoring autocorrelated data. Therefore, an integrated model subject to some constraints is proposed to determine the optimal decision variables of the ARMA control chart, economic production quantity, and maintenance policy in the presence of autocorrelated data. Due to the complexity of the model, a particle swarm optimization (PSO) algorithm is applied to search for optimal decision variables. An industrial example and some comparisons are provided for more investigations. Moreover, sensitivity analysis is carried out to study the effects of model parameters on the solution of the economic-statistical design.

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Section: Discussionmentioning

confidence: 99%

An integrated model for optimal selection of quality, maintenance, and production parameters with auto correlated data

et al. 2021

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“…In other papers such as [10][11][12][13][14][15][16][17][18][19], more proposals for improving the np control chart can be found.…”

Section: Introductionmentioning

confidence: 99%

Optimized np Attribute Control Chart Using Triple Sampling

2022

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This paper studies an attribute control chart for monitoring the number of nonconforming items using a triple sampling (TS-np) which has not yet been applied to attribute control charts. The chart design and procedure for the decision about the state of the process are given. Mathematical expressions for the average run length (ARL) for in-control and out-of-control processes and the average sample number (ASN) are given. A bi-objective genetic algorithm that seeks to minimize the ASN and the probability of type 2 error is implemented in order to optimize the design of the TS-np control chart. A comparison between TS-np, single sampling np (SS-np), double sampling np (DS-np), and multiple dependent state repetitive sampling (MDSRS) control charts is carried out in terms of the out-of-control average run length (). Tables of values for TS-np are presented in comparison with MDSRS and DS-np for various scenarios. The operation of the proposed control chart is shown through simulated data. Finally, it is concluded that the proposed TS-np chart has a better performance in terms of detecting small and moderate shifts in the process nonconforming rate in-control () compared with MDSRS and DS-np.

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“…Finally, if a sample falls outside the control limits, the sample belongs to the out-of-control region and the process is considered to be out-of-control, where corrective action(s) should be taken to remove the assignable cause(s). Some of the recent studies on VSI charts were made by Haq [23], Khoo et al [24] and Shojaee et al [25].…”

Section: Introductionmentioning

confidence: 99%

A Variable Sampling Interval Multivariate Exponentially Weighted Moving Average Control Chart for Monitoring the Gumbel’s Bivariate Exponential Data

Xie

Qiao

et al. 2022

Scientia Iranica

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The general assumption in designing a multivariate control chart is that the multiple variables are independent and normally distributed. This assumption may not be tenable in many practical situations, because multiple variables with dependency often need to be monitored simultaneously to ensure the process is in control. The Gumbel's bivariate exponential (GBE) distribution is considered to be a better model for skewed data with dependency in reliability analysis. In this paper, a multivariate exponentially weighted moving average (MEWMA) scheme with variable sampling interval (VSI) feature is developed to monitor the mean vector of GBE model. The Monte Carlo simulation is used to evaluate the average time to signal (ATS) performance of the proposed VSI MEWMA GBE scheme for three different types of shifts. Some tables are presented to show the ATS performance of the proposed scheme with different designed parameters. Additionally, both the zero-state and the steady-state ATS performance of the proposed scheme is compared with that of the conventional MEWMA chart with FSI (Fix Sampling Interval) feature. Comparative results show that the suggested scheme works better than its FSI counterpart in monitoring GBE data. Finally, a simulation example is provided to show that the VSI MEWMA GBE scheme performs well in monitoring GBE data.

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Applying SVSSI sampling scheme on np-chart to Decrease the Time of Detecting Shifts - Markov chain approach, and Monte Carlo simulation

Cited by 4 publications

References 0 publications

An integrated model for optimal selection of quality, maintenance, and production parameters with auto correlated data

An integrated model for optimal selection of quality, maintenance, and production parameters with auto correlated data

Optimized np Attribute Control Chart Using Triple Sampling

A Variable Sampling Interval Multivariate Exponentially Weighted Moving Average Control Chart for Monitoring the Gumbel’s Bivariate Exponential Data

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