Economic-Statistical Design of Integrated Model of VSI Control Chart and Maintenance Incorporating Multiple Dependent State Sampling

Wan, Qiang

doi:10.1109/access.2020.2993024

Cited by 8 publications

(3 citation statements)

References 35 publications

(37 reference statements)

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“…Meta-heuristic algorithms, especially genetic algorithm (GA), have been proven to be suitable for optimizing such problems and it has a high probability of finding a global optimum solution on most occasions. 27 Successful applications of GA in the optimization design of control charts can be seen in Sabahno et al, 28 Wang and Li, 29 Wan 30 and so on. Due to the flexibility and adaptive nature of GA in solving this kind of optimization problems, a GA will be developed to optimize the minimization problem for statistical design of the joint X ¯ / R : r / m chart.…”

Section: Design Modelmentioning

confidence: 99%

Optimal design of an improved X¯ and R control chart for joint monitoring of process location and dispersion

Wan

Mei

2022

Measurement and Control

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Enhancing the detection power of control charts for detecting small to moderate process changes is always the focus of attention in academia. To improve the detection ability of conventional [Formula: see text] and R control charts, an improved joint [Formula: see text] and R chart, which combines the ordinary [Formula: see text] and R charts with runs rules of the type ‘ r out of m’, is proposed to monitor the process location and dispersion simultaneously. A finite Markov chain imbedding approach is employed to develop the resulting control scheme. A comparative study is conducted to investigate the performance of the proposed chart in terms of the out-of-control average run length. The statistical performance of the suggested chart when the process parameters are estimated is also evaluated. The numerical results indicate that (1) the proposed chart improves the detection ability of traditional [Formula: see text] and R charts in detecting small to moderate process shifts; (2) the suggested scheme performs better than the EWMA and CUSUM schemes in detecting large process fluctuations. Furthermore, when specific values of r and m are selected, the statistical performance of the proposed chart for detecting small shifts is close to or even better than that of its competitors; (3) the run length performance of the proposed chart is greatly affected by parameter estimation, especially for small process shifts.

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Section: Design Modelmentioning

confidence: 99%

Optimal design of an improved X¯ and R control chart for joint monitoring of process location and dispersion

Wan

Mei

2022

Measurement and Control

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“…Furthermore, Lesage and Dehombreux (2015) formulated a simulation model for an imperfect system subject to periodic PM, CM at failure and quality-based maintenance if the control limits are exceeded. Wan (2020) proposed an integrated model of statistical process control and maintenance using an adaptive control chart and the multiple dependent/deferred state sampling (MDSS) policy. If the chart sends an out-of-control signal, a reactive maintenance action is performed to handle the true signal ("out-of-control" state) in order to bring the process back to the "incontrol" state.…”

Section: Control Chart Production and Maintenancementioning

confidence: 99%

Joint design of control chart, production and maintenance policy for unreliable manufacturing systems

Bahria

Dridi

Chelbi

et al. 2020

JQME

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PurposeThe purpose of this study is to develop a joint production, maintenance and quality control strategy involving a periodic preventive maintenance policy.Design/methodology/approachThe proposed integrated policy is defined and modeled mathematically.FindingsThe paper focuses on finding simultaneously the optimal values of the preventive maintenance period, the buffer stock size, the sample size, the sampling interval and the control chart limits, such that the expected total cost per time unit is minimized.Practical implicationsThe paper attempts to integrate in a single model the three main aspects of any manufacturing system: production, maintenance and quality. The considered system consists of one machine subject to a degradation process that directly affects the quality of products. The process and product quality control is carried out using an “x-bar” control chart. In the proposed model, a preventive maintenance action is performed every α inspections of product quality in order to reduce the shift rate to the “out-of-control” state. A corrective maintenance action is undertaken once the control limits are exceeded. In order to palliate perturbations caused by the stopping of the machine to undergo maintenance actions, a buffer stock is built up to ensure the continuous supply of the subsequent machine. The main goal of this work is to develop a model that captures the underlying link between the preventive maintenance policy, the buffer stock size and the parameters of an “x-bar” control chart used to control the quality of the product. Numerical experiments and a study of the effects of the input parameters variation on the obtained results are performed.Originality/valueThe existing models that simultaneously consider maintenance, inventory and control charts consist of a condition-based maintenance (CBM) policy. Periodic preventive maintenance (PM) has not been considered in such models. The proposed integrated model is original, in that it links production through buffer stocks, quality through a control chart and maintenance through periodic preventive maintenance (different practical settings and modeling approach than when CBM is used). Hence, this paper addresses practical situations where, for economic or technical reasons, only systematic periodic preventive maintenance is possible.

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“…For instance, Rao et al [2] proposed the use of the 𝐶 chart with the GMDS (Generalized Multiple Dependent State Sampling) supplementary signaling rule, that means the 𝐶 chart signals when a point falls in the action region or, yet, when a point falling in the warning region is preceded by a sequence of m points with less than k in the central region. If 𝑘 = 𝑚, the GMDS reduces to the MDS (Multiple Dependent State Sampling) [3]- [6]. The multiple dependent state sampling has also been used in combination with repetitive sampling, MDSRS [7]- [14].…”

Section: Introductionmentioning

confidence: 99%

Comparing the Performance of C _pk and X Control Charts With the Generalized Multiple Dependent State Sampling Supplementary Signaling Rule

Costa

2022

IEEE Access

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The 𝐶 index was originally created to measure the ability of the processes to produce products meeting specifications but, more recently, Rao and team proposed the use of the 𝐶 index to control processes. In reality, they investigated the performance of the 𝐶 chart with the GMDS (Generalized Multiple Dependent State Sampling) supplementary signaling rule. With the GMDS supplementary run rule, the 𝐶 chart signals when a point falls in the action region or when a point falls in the warning region after a sequence of m points, with less than k, in the central region. The points on the 𝐶 chart are the estimated values of the 𝐶 index obtained with the mean and the variance of the samples. Rao and team obtained the ARLs of the GMDS 𝐶 chart by simulation; reminding the ARL is the average number of samples the chart requires to signal a change in the process. This present work uses the Markov chain approach to obtain the ARLs, once this approach leads to exact results. The main conclusion is that the GMDS 𝑋 chart is not only simpler to use than the GMDS 𝐶 chart, but it is also more sensitive to process changes.INDEX TERMS Control charts, generalized multiple dependent state sampling, process capability index.

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Economic-Statistical Design of Integrated Model of VSI Control Chart and Maintenance Incorporating Multiple Dependent State Sampling

Cited by 8 publications

References 35 publications

Optimal design of an improved X¯ and R control chart for joint monitoring of process location and dispersion

Optimal design of an improved X¯ and R control chart for joint monitoring of process location and dispersion

Joint design of control chart, production and maintenance policy for unreliable manufacturing systems

Comparing the Performance of C _pk and X Control Charts With the Generalized Multiple Dependent State Sampling Supplementary Signaling Rule

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Economic-Statistical Design of Integrated Model of VSI Control Chart and Maintenance Incorporating Multiple Dependent State Sampling

Cited by 8 publications

References 35 publications

Optimal design of an improved X¯ and R control chart for joint monitoring of process location and dispersion

Optimal design of an improved X¯ and R control chart for joint monitoring of process location and dispersion

Joint design of control chart, production and maintenance policy for unreliable manufacturing systems

Comparing the Performance of C pk and X Control Charts With the Generalized Multiple Dependent State Sampling Supplementary Signaling Rule

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Comparing the Performance of C _pk and X Control Charts With the Generalized Multiple Dependent State Sampling Supplementary Signaling Rule