scholarly journals Synthetic control charts with two-stage sampling for monitoring bivariate processes

2007 ◽  
Vol 27 (1) ◽  
pp. 117-130 ◽  
Author(s):  
Antonio F. B. Costa ◽  
Marcela A. G. Machado
Keyword(s):  
Sample Size ◽  
Control Chart ◽  
Control Charts ◽  
Quality Characteristics ◽  
Synthetic Control ◽  
Two Stage ◽  
Variable Sample Size ◽  
Second Stage ◽  
Process Disturbances

In this article, we consider the synthetic control chart with two-stage sampling (SyTS chart) to control bivariate processes. During the first stage, one item of the sample is inspected and two correlated quality characteristics (x;y) are measured. If the Hotelling statistic T1² for these individual observations of (x;y) is lower than a specified value UCL1 the sampling is interrupted. Otherwise, the sampling goes on to the second stage, where the remaining items are inspected and the Hotelling statistic T2² for the sample means of (x;y) is computed. When the statistic T2² is larger than a specified value UCL2, the sample is classified as nonconforming. According to the synthetic control chart procedure, the signal is based on the number of conforming samples between two neighbor nonconforming samples. The proposed chart detects process disturbances faster than the bivariate charts with variable sample size and it is from the practical viewpoint more convenient to administer.

Fuzzy U control chart based on fuzzy rules and evaluating its performance using fuzzy OC curve

2018 ◽  
Vol 30 (3) ◽  
pp. 232-247 ◽  
Author(s):  
Somayeh Fadaei ◽  
Alireza Pooya
Keyword(s):  
Fuzzy Control ◽  
Sample Size ◽  
Control Chart ◽  
Control Charts ◽  
Operating Characteristic ◽  
Fuzzy Rules ◽  
Type Ii ◽  
Type Ii Error ◽  
Content Type ◽  
Variable Sample Size

Purpose The purpose of this paper is to apply fuzzy spectrum in order to collect the vague and imprecise data and to employ the fuzzy U control chart in variable sample size using fuzzy rules. This approach is improved and developed by providing some new rules. Design/methodology/approach The fuzzy operating characteristic (FOC) curve is applied to investigate the performance of the fuzzy U control chart. The application of FOC presents fuzzy bounds of operating characteristic (OC) curve whose width depends on the ambiguity parameter in control charts. Findings To illustrate the efficiency of the proposed approach, a practical example is provided. Comparing performances of control charts indicates that OC curve of the crisp chart has been located between the FOC bounds, near the upper bound; as a result, for the crisp control chart, the probability of the type II error is of significant level. Also, a comparison of the crisp OC curve with OCavg curve and FOCα curve approved that the probability of the type II error for the crisp chart is more than the same amount for the fuzzy chart. Finally, the efficiency of the fuzzy chart is more than the crisp chart, and also it timely gives essential alerts by means of linguistic terms. Consequently, it is more capable of detecting process shifts. Originality/value This research develops the fuzzy U control chart with variable sample size whose output is fuzzy. After creating control charts, performance evaluation in the industry is important. The main contribution of this paper is to employs the FOC curve for evaluating the performance of the fuzzy control chart, while in prior studies in this area, the performance of fuzzy control chart has not been evaluated.

Two-Stage Control Charts for High Yield Processes

1997 ◽  
Vol 04 (02) ◽  
pp. 149-165 ◽  
Author(s):  
L. Y. Chan ◽  
M. Xie ◽  
T. N. Goh
Keyword(s):  
False Alarm ◽  
Control Chart ◽  
Control Charts ◽  
High Yield ◽  
Two Stage ◽  
Defective Items ◽  
Second Stage ◽  
Order Of Magnitude ◽  
Cumulative Count

In this paper, a two-stage control chart for monitoring the defective rate of high-yield processes is proposed and studied. The Cumulative Count of Conforming control chart is generalized by using the number of items inspected until two defective items are observed. As this will increase the time to alarm, a two-stage approach combining both schemes is proposed. The occurrence of a defective within n1 items inspected in the first stage indicates that the process is out of control. If no defective occurs within n1 items inspected, the occurrence of two defectives within the next n2 - n1 in the second stage also indicates that the process is out of control. The probability of making a false alarm at the first and second stages are equal to α1 and α2, respectively. This procedure improves the sensitivity of the control chart in detecting shifting of the process defective rate p when p is at the parts-per-million order of magnitude.

scholarly journals Multivariate Control Chart Based on Kernel PCA for Monitoring Mixed Variable and Attribute Quality Characteristics

Symmetry ◽  
2020 ◽  
Vol 12 (11) ◽  
pp. 1838
Author(s):  
Muhammad Ahsan ◽  
Muhammad Mashuri ◽  
Wibawati ◽  
Hidayatul Khusna ◽  
Muhammad Hisyam Lee
Keyword(s):  
Control Chart ◽  
Control Charts ◽  
Synthetic Data ◽  
Principal Component ◽  
Quality Characteristics ◽  
Kernel Functions ◽  
Quality Characteristic ◽  
Kernel Pca ◽  
Asymmetric Pattern ◽  
Attribute Quality

The need for a control chart that can visualize and recognize the symmetric or asymmetric pattern of the monitoring process with more than one type of quality characteristic is a necessity in the era of Industry 4.0. In the past, the control charts were only developed to monitor one kind of quality characteristic. Several control charts were created to deal with this problem. However, there are some problems and drawbacks to the conventional mixed charts. In this study, another approach is used to monitor mixed quality characteristics by applying the Kernel Principal Component Analyisis (KPCA) method. Using the Hotelling’s T2 statistic, the kernel PCA mix chart is proposed to simultaneously monitor the variable and attribute quality characteristics. Due to its ability to estimate the asymmetric pattern of the mixed process, the kernel density estimation (KDE) used in the proposed chart has successfully estimated the control limits that produce ARL0 at about 370 for α=0.00273. Through several experiments based on the proportion of the attribute characteristics and kernel functions, the proposed chart demonstrates better performance in detecting outlier and shift in the process. When it is applied to monitor the synthetic data, the proposed chart can detect the shift accurately. Additionally, the proposed chart outperforms the performance of the conventional mixed chart based on PCA mix by producing lower false alarm with more accurate detection of out of control processes.

Monitoring the coefficient of variation using a variable sample size control chart

2015 ◽  
Vol 80 (9-12) ◽  
pp. 1561-1576 ◽  
Author(s):  
Philippe Castagliola ◽  
Ali Achouri ◽  
Hassen Taleb ◽  
Giovanni Celano ◽  
Stelios Psarakis
Keyword(s):  
Sample Size ◽  
Control Chart ◽  
Coefficient Of Variation ◽  
Size Control ◽  
Variable Sample Size

scholarly journals Monitoring Mortality Caused by COVID-19 Using Gamma-Distributed Variables Based on Generalized Multiple Dependent State Sampling

2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
Muhammad Aslam ◽  
G. Srinivasa Rao ◽  
Muhammad Saleem ◽  
Rehan Ahmad Khan Sherwani ◽  
Chi-Hyuck Jun
Keyword(s):  
Control Chart ◽  
Control Charts ◽  
Average Run Length ◽  
Real Life ◽  
Quality Characteristics ◽  
Statistical Quality Control ◽  
Nonnormal Distributions ◽  
Run Lengths ◽  
Chart Design ◽  
Dependent State

More recently in statistical quality control studies, researchers are paying more attention to quality characteristics having nonnormal distributions. In the present article, a generalized multiple dependent state (GMDS) sampling control chart is proposed based on the transformation of gamma quality characteristics into a normal distribution. The parameters for the proposed control charts are obtained using in-control average run length (ARL) at specified shape parametric values for different specified average run lengths. The out-of-control ARL of the proposed gamma control chart using GMDS sampling is explored using simulation for various shift size changes in scale parameters to study the performance of the control chart. The proposed gamma control chart performs better than the existing multiple dependent state sampling (MDS) based on gamma distribution and traditional Shewhart control charts in terms of average run lengths. A case study with real-life data from ICU intake to death caused by COVID-19 has been incorporated for the realistic handling of the proposed control chart design.

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