Journal of Industrial Engineering, International, Dec 1, 2020
In many processes, quality characteristic is identified by the regression relationship between on... more In many processes, quality characteristic is identified by the regression relationship between one or more dependent variables and one or more independent variables called profile. In this paper, a control chart based on discriminant analysis (DA) is proposed to monitor simple linear profiles in Phase II. A chi-square control chart joined with DA chart is also used to improve detecting variance shifts. Performance of the proposed method is evaluated in terms of average run length using Monte-Carlo simulations. Performance of the proposed control chart is compared to the basic methods in simple linear profile monitoring. Results present the desirable performance of the proposed method. The real case in shoes leather industry is also investigated to show the effectiveness of the proposed method. results also confirm an acceptable performance of the real case, because the average run length of the proposed control chart is less than the average run length of the comparable method.
Communications in Statistics - Simulation and Computation, Mar 1, 2012
In certain statistical process control applications, performance of a product or process can be m... more In certain statistical process control applications, performance of a product or process can be monitored effectively using a linear profile or a linear relationship between a response variable and one or more explanatory variables. In this article, we design a nonparametric bootstrap control chart for monitoring simple linear profiles based on T statistic. We evaluate the performance of the proposed method in phase II. The average and standard deviation of the run length under different shifts in the intercept, slope, and standard deviation are considered as the performance measures. Simulation results show that the performance of the proposed bootstrap control chart improves as the size of the available data increases.
In this paper, a maximum likelihood estimator is developed to estimate isotonic change point in t... more In this paper, a maximum likelihood estimator is developed to estimate isotonic change point in the parameters of a polynomial profile in phase II. In addition, performance of the proposed estimator is compared to the performance of the step change point estimator, under increasing change types using simulation study. Accuracy and the precision of the estimators are considered as the performance measures in this paper. Simulation results show that the proposed estimator has an acceptable performance in terms of the accuracy and precision of the estimations. The proposed estimator also does not require any awareness about the change type, and its only assumption is that changes occur in an increasing manner. This is the advantage of the proposed estimator over the step change point estimator.
This study compares two proposed mixed quick switching sampling (QSS) plans for linear profiles a... more This study compares two proposed mixed quick switching sampling (QSS) plans for linear profiles as the quality characteristic. For the QSS plans, we recommend a binomial attribute plan for normal inspection and then a variable sampling plan for tightened inspection based on capability index CpuA of linear profiles with one‐sided specifications. The difference between the two proposed QSS plans is in the tightened inspection. Tightened inspection of the first proposed plan is a single sampling using CpuA index, but tightened inspection of the second plan is a multiple dependent state repetitive (MDSR) plan based on CpuA index. The optimal parameters are obtained by nonlinear optimization. Simulation study for selecting parameters is conducted with various combinations of specified acceptable quality level (AQL), limited quality level (LQL), producer's risk, and consumer's risk. Simulation results confirm that the second proposed QSS plan which applies variable MDSR at tighten...
Journal of Industrial Engineering, International, 2020
In many processes, quality characteristic is identified by the regression relationship between on... more In many processes, quality characteristic is identified by the regression relationship between one or more dependent variables and one or more independent variables called profile. In this paper, a control chart based on discriminant analysis (DA) is proposed to monitor simple linear profiles in Phase II. A chi-square control chart joined with DA chart is also used to improve detecting variance shifts. Performance of the proposed method is evaluated in terms of average run length using Monte-Carlo simulations. Performance of the proposed control chart is compared to the basic methods in simple linear profile monitoring. Results present the desirable performance of the proposed method. The real case in shoes leather industry is also investigated to show the effectiveness of the proposed method. results also confirm an acceptable performance of the real case, because the average run length of the proposed control chart is less than the average run length of the comparable method.
In certain statistical process control applications, performance of a product or process can be m... more In certain statistical process control applications, performance of a product or process can be monitored effectively using a linear profile or a linear relationship between a response variable and one or more explanatory variables. In this article, we design a nonparametric bootstrap control chart for monitoring simple linear profiles based on T statistic. We evaluate the performance of the proposed method in phase II. The average and standard deviation of the run length under different shifts in the intercept, slope, and standard deviation are considered as the performance measures. Simulation results show that the performance of the proposed bootstrap control chart improves as the size of the available data increases.
Journal of Industrial Engineering, International, Dec 1, 2020
In many processes, quality characteristic is identified by the regression relationship between on... more In many processes, quality characteristic is identified by the regression relationship between one or more dependent variables and one or more independent variables called profile. In this paper, a control chart based on discriminant analysis (DA) is proposed to monitor simple linear profiles in Phase II. A chi-square control chart joined with DA chart is also used to improve detecting variance shifts. Performance of the proposed method is evaluated in terms of average run length using Monte-Carlo simulations. Performance of the proposed control chart is compared to the basic methods in simple linear profile monitoring. Results present the desirable performance of the proposed method. The real case in shoes leather industry is also investigated to show the effectiveness of the proposed method. results also confirm an acceptable performance of the real case, because the average run length of the proposed control chart is less than the average run length of the comparable method.
Communications in Statistics - Simulation and Computation, Mar 1, 2012
In certain statistical process control applications, performance of a product or process can be m... more In certain statistical process control applications, performance of a product or process can be monitored effectively using a linear profile or a linear relationship between a response variable and one or more explanatory variables. In this article, we design a nonparametric bootstrap control chart for monitoring simple linear profiles based on T statistic. We evaluate the performance of the proposed method in phase II. The average and standard deviation of the run length under different shifts in the intercept, slope, and standard deviation are considered as the performance measures. Simulation results show that the performance of the proposed bootstrap control chart improves as the size of the available data increases.
In this paper, a maximum likelihood estimator is developed to estimate isotonic change point in t... more In this paper, a maximum likelihood estimator is developed to estimate isotonic change point in the parameters of a polynomial profile in phase II. In addition, performance of the proposed estimator is compared to the performance of the step change point estimator, under increasing change types using simulation study. Accuracy and the precision of the estimators are considered as the performance measures in this paper. Simulation results show that the proposed estimator has an acceptable performance in terms of the accuracy and precision of the estimations. The proposed estimator also does not require any awareness about the change type, and its only assumption is that changes occur in an increasing manner. This is the advantage of the proposed estimator over the step change point estimator.
This study compares two proposed mixed quick switching sampling (QSS) plans for linear profiles a... more This study compares two proposed mixed quick switching sampling (QSS) plans for linear profiles as the quality characteristic. For the QSS plans, we recommend a binomial attribute plan for normal inspection and then a variable sampling plan for tightened inspection based on capability index CpuA of linear profiles with one‐sided specifications. The difference between the two proposed QSS plans is in the tightened inspection. Tightened inspection of the first proposed plan is a single sampling using CpuA index, but tightened inspection of the second plan is a multiple dependent state repetitive (MDSR) plan based on CpuA index. The optimal parameters are obtained by nonlinear optimization. Simulation study for selecting parameters is conducted with various combinations of specified acceptable quality level (AQL), limited quality level (LQL), producer's risk, and consumer's risk. Simulation results confirm that the second proposed QSS plan which applies variable MDSR at tighten...
Journal of Industrial Engineering, International, 2020
In many processes, quality characteristic is identified by the regression relationship between on... more In many processes, quality characteristic is identified by the regression relationship between one or more dependent variables and one or more independent variables called profile. In this paper, a control chart based on discriminant analysis (DA) is proposed to monitor simple linear profiles in Phase II. A chi-square control chart joined with DA chart is also used to improve detecting variance shifts. Performance of the proposed method is evaluated in terms of average run length using Monte-Carlo simulations. Performance of the proposed control chart is compared to the basic methods in simple linear profile monitoring. Results present the desirable performance of the proposed method. The real case in shoes leather industry is also investigated to show the effectiveness of the proposed method. results also confirm an acceptable performance of the real case, because the average run length of the proposed control chart is less than the average run length of the comparable method.
In certain statistical process control applications, performance of a product or process can be m... more In certain statistical process control applications, performance of a product or process can be monitored effectively using a linear profile or a linear relationship between a response variable and one or more explanatory variables. In this article, we design a nonparametric bootstrap control chart for monitoring simple linear profiles based on T statistic. We evaluate the performance of the proposed method in phase II. The average and standard deviation of the run length under different shifts in the intercept, slope, and standard deviation are considered as the performance measures. Simulation results show that the performance of the proposed bootstrap control chart improves as the size of the available data increases.
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