Optimal performance of the variable sample sizes Hotelling’s <i>T</i><sup>2</sup> control chart in the presence of measurement errors

Sabahno, Hamed; Amiri, Amirhossein; Castagliola, Philippe

doi:10.1080/16843703.2018.1490474

Cited by 29 publications

(24 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…These results are completely expected when the objective function is

\overset{ATS}{true}

. However, if we choose a different objective function, different results for the optimal chart parameters values are to be expected (see Sabahno et al).…”

Section: Simulation Studiesmentioning

confidence: 89%

“…As in Sabahno et al, it is assumed that Y ij is a p × 1 vector of quality characteristics. Samples are taken every i = 1,2,3,… t.u.…”

Section: Measurement Errors Modelmentioning

confidence: 99%

“…Evaluating the effect of measurement errors on adaptive mean control charts has been performed by Hu et al, Sabahno and Amiri, and Sabahno et al for VSI, VSS, VSSI, and VP schemes, respectively. For adaptive multivariate mean vector charts, the effects of measurement errors have only been considered in Sabahno et al for VSS and VSI schemes, respectively.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A variable parameters multivariate control chart for simultaneous monitoring of the process mean and variability with measurement errors

Sabahno

Castagliola

Amiri

2020

Quality & Reliability Eng

Self Cite

View full text Add to dashboard Cite

Evaluating the effect of measurement errors on either adaptive or simultaneous control charts has been a topic of interest for the researchers in the recent years. Nevertheless, the effect of measurement errors on both adaptive and simultaneous monitoring control charts has not been considered yet. In this paper, through extensive numerical studies, we evaluate the effect of measurement errors on an adaptive (variable parameters) simultaneous multivariate control chart for the mean vector and the variancecovariance matrix of p quality characteristics assumed to follow a multivariate normal distribution. In order to do so, (a) we use eight performance measures computed using a Markov chain model, (b) we consider the effects of multiple measurements as well as the error model's parameters, and (c) we also consider the overall performance of this adaptive simultaneous chart including the chart parameters values optimization, which have never been considered so far for this scheme. At last, a real case is presented in order to illustrate the proposed scheme. K E Y W O R D S

show abstract

“…These results are completely expected when the objective function is

\overset{ATS}{true}

. However, if we choose a different objective function, different results for the optimal chart parameters values are to be expected (see Sabahno et al).…”

Section: Simulation Studiesmentioning

confidence: 89%

“…As in Sabahno et al, it is assumed that Y ij is a p × 1 vector of quality characteristics. Samples are taken every i = 1,2,3,… t.u.…”

Section: Measurement Errors Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A variable parameters multivariate control chart for simultaneous monitoring of the process mean and variability with measurement errors

Sabahno

Castagliola

Amiri

2020

Quality & Reliability Eng

Self Cite

View full text Add to dashboard Cite

show abstract

“…The SPM literature of approximately 60 articles dealing with measurement errors are reviewed in Maleki et al For some more recent research works on measurement errors, see Yeong et al, Zaidi et al, Sabahno et al, Tang et al, Nguyen et al, and Tran et al A number of methods used as remedial approaches are outlined in Maleki et al, and the most used methodology to reduce measurement inaccuracy is by taking multiple measurements of each item, which was first proposed by Linna and Woodall . The multiple measurements approach keeps being investigated in research works to this day; hence, in this paper too, this remedial approach for measurement inaccuracy is implemented.…”

Section: Introductionmentioning

confidence: 99%

On monitoring the process mean of autocorrelated observations with measurement errors using the w‐of‐w runs‐rules scheme

Shongwe

Malela‐Majika

Castagliola

2020

Quality & Reliability Eng

Self Cite

View full text Add to dashboard Cite

Runs‐rules have been widely used since the 1950s in industrial and nonindustrial process monitoring applications to improve the performance of basic and other traditional monitoring schemes. However, none of the studies on runs‐rules have accounted for a process with a combined effect of measurement errors and autocorrelation. Hence, in this paper, the use of the w‐of‐w runs‐rules to improve the performance of the Shewhart trueX¯ scheme using an additive model with a constant variance and a first‐order autoregressive model is proposed. To reduce the combined negative effect of measurement errors and autocorrelation, we implement a sampling strategy based on rational subgroups in which (a) multiple measurements per item are taken (instead of a standard single measurement) and (b) non‐neighboring observations are gathered. Moreover, the latter sampling strategy is incorporated into the values of probability elements of a Markov chain matrix which is used to derive some closed‐form expressions for the zero‐ and steady‐state run‐length distribution. The main finding of this study is that, with respect to some overall performance measures, the proposed scheme outperforms the existing Shewhart trueX¯ scheme by a significant margin. A real‐life example is used to illustrate the practical implementation of the proposed scheme.

show abstract

“…Furthermore, the multivariate control chart is a control chart used to control production process with more than one correlated or uncorrelated characteristics. The latest investigation of the multivariate control chart includes of [10][11][12][13][14][15][16][17][18].…”

Section: Introductionmentioning

confidence: 99%

Intrusion Detection System Using Multivariate Control Chart Hotelling's T2 Based on PCA

Ahsan¹,

Mashuri²,

Kuswanto³

et al. 2018

Int. J. Adv. Sci. Eng. Inf. Technol.

View full text Add to dashboard Cite

Statistical Process Control (SPC) has been widely used in industry and services. The SPC can be applied not only to monitor manufacture processes but also can be applied to the Intrusion Detection System (IDS). In network monitoring and intrusion detection, SPC can be a powerful tool to ensure system security and stability in a network. Theoretically, Hotelling's T 2 chart can be used in intrusion detection. However, there are two reasons why the chart is not suitable to be used. First, the intrusion detection data involves large volumes of high-dimensional process data. Second, intrusion detection requires a fast computational process so an intrusion can be detected as soon as possible. To overcome the problems caused by large number of quality characteristics, Principal Component Analysis (PCA) can be used. The PCA can reduce not only the dimension leading a faster computational, but also can eliminate the multicollinearity (among characteristic variables) problem. This paper is focused on the usage of multivariate control chart T 2 based on PCA for IDS. KDD99 dataset is used to evaluate the performance of the proposed method. Furthermore, the performance of T 2 based PCA will be compared with conventional T 2 control chart. The empirical results of this research show that the multivariate control chart using Hotelling's T 2 based on PCA has excellent performance to detect anomaly in network. Compared to conventional T 2 control chart, the T 2 based on PCA has similar performance with 97 percent hit rate. It also requires shorter computation time.

show abstract

Optimal performance of the variable sample sizes Hotelling’s T² control chart in the presence of measurement errors

Cited by 29 publications

References 31 publications

A variable parameters multivariate control chart for simultaneous monitoring of the process mean and variability with measurement errors

A variable parameters multivariate control chart for simultaneous monitoring of the process mean and variability with measurement errors

On monitoring the process mean of autocorrelated observations with measurement errors using the w‐of‐w runs‐rules scheme

Intrusion Detection System Using Multivariate Control Chart Hotelling's T2 Based on PCA

Contact Info

Product

Resources

About

Optimal performance of the variable sample sizes Hotelling’s T2 control chart in the presence of measurement errors

Cited by 29 publications

References 31 publications

A variable parameters multivariate control chart for simultaneous monitoring of the process mean and variability with measurement errors

A variable parameters multivariate control chart for simultaneous monitoring of the process mean and variability with measurement errors

On monitoring the process mean of autocorrelated observations with measurement errors using the w‐of‐w runs‐rules scheme

Intrusion Detection System Using Multivariate Control Chart Hotelling's T2 Based on PCA

Contact Info

Product

Resources

About

Optimal performance of the variable sample sizes Hotelling’s T² control chart in the presence of measurement errors