Statistical Monitoring of Autocorrelated Simple Linear Profiles Based on Principal Components Analysis

Niaki, Seyed Taghi Akhavan; Khedmati, Majid; Soleymanian, Mir Emad

doi:10.1080/03610926.2013.835417

Cited by 17 publications

(12 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this phase, the process stability is evaluated and the process parameters under in-control status are estimated. In Phase II, the estimated parameters in Phase I are used to design the control chart for monitoring the process over time and detecting any shifts in the process [2,3]. Woodall et al [4] presented an introduction as well as a literature review on pro le monitoring.…”

Section: Introductionmentioning

confidence: 99%

Robust Holt-Winter Based Control Chart for Monitoring Autocorrelated Simple Linear Profiles With Contaminated Data

Kamranrad

Amiri

2016

Scientia Iranica

View full text Add to dashboard Cite

Pro le monitoring is a useful technique in statistical process control used when quality of the product or process is represented by a function over a time period. This function represents the relationship between a response variable and one or more explanatory variables. Most existing control charts for monitoring pro les are based on the assumption that the observations within each pro le are independent of each other, which is often violated in practice. Sometimes there are one or more outliers in each pro le which lead to poor statistical performance of the control chart. This paper focuses on Phase II monitoring of a simple linear pro le with autocorrelation within pro le data in the presence of outliers. In this paper, we propose a new combined control chart based on the robust Holt-Winter model to decrease the e ect of outliers. We rst evaluate the e ect of outliers on the performance of the proposed combined control chart. Then, we apply robust Holt-Winter and design a robust combined control chart to overcome the e ect of outliers. The performance of the proposed robust Holt-Winter control chart is evaluated through extensive simulation studies. The results show that the proposed robust control chart performs well.

show abstract

Section: Introductionmentioning

confidence: 99%

Robust Holt-Winter Based Control Chart for Monitoring Autocorrelated Simple Linear Profiles With Contaminated Data

Kamranrad

Amiri

2016

Scientia Iranica

View full text Add to dashboard Cite

show abstract

“…Amiri et al applied 3 methods, namely, T 2 , likelihood ratio test, and F method, and Shadman et al used a change point method to control the generalized linear models (GLMs). Niaki et al studied the autocorrelated simple linear profiles based on the principal component analysis. Jensen et al studied correlation within linear profiles by using mixed models.…”

Section: Introductionmentioning

confidence: 99%

Monitoring simple linear profiles in the presence of generalized autoregressive conditional heteroscedasticity

Hadizadeh

Soleimani

2017

Quality & Reliability Eng

View full text Add to dashboard Cite

In this paper, monitoring of simple linear profiles is investigated in the presence of nonequality of variances or heteroscedasticity, ie, generalized autoregressive conditional heteroscedasticity. In this condition, using of the common methods regardless of the heteroscedasticity leads to the fault interpretations. We consider a simple linear profile and assume that there is a generalized autoregressive conditional heteroscedasticity (GARCH) (1,1) model within the profiles. Here, we particularly focus on Phase II monitoring of simple linear regression. We studied the generalized autoregressive conditional heteroscedasticity effect, briefly GARCH effect, on the average run length criterion. As the remedial measures, the weighted least squares method to estimate the regression parameters and the heteroscedasticity‐consistent approaches to estimate the covariance matrix of regression parameters, are used to extract the GARCH effect. Two control chart methods namely T2 and exponentially weighted moving average 3 are discussed to monitor the simple linear profiles. Their performances are evaluated by using the average run length criterion. Finally, a real case from an industry field is studied.

show abstract

“…Jensen and Birch, Noorossana et al ,. Yeh and Zerehsaz, and Niaki et al . investigated Phase I methods, while some others including Kang and Albin, Kim et al ,.…”

Section: Introduction and Literature Reviewmentioning

confidence: 99%

“…In Phase I, one evaluates the process stability and estimates its parameters based on a historical set of data, while the purpose of Phase II analysis is to detect shifts in the process parameters as soon as possible. Some researchers such as Mestek et al 4 , Stover and Brill 1 , Mahmoud and Woodall 5 , Mahmoud et al 6 , Jensen et al 7 , Jensen and Birch 8 , Noorossana et al 9 , Yeh and Zerehsaz 10 , and Niaki et al 11 investigated Phase I methods, while some others including Kang and Albin 2 , Kim et al 12 , Gupta et al 13 , Zou et al 14 , Niaki et al 15 , Zou et al 16 , Zhang et al 17 , Eyvazian et al 18 , Mahmoud 19 , Xu et al 20 , Soleymanian et al 21 , Amiri et al 22 , Noorossana et al 23 , Huwang et al 24 , and Huwang et al 25 studied Phase II procedures.…”

mentioning

confidence: 99%

Phase II monitoring of general linear profiles in the presence of between-profile autocorrelation

Khedmati

Niaki

2015

Qual. Reliab. Engng. Int.

Self Cite

View full text Add to dashboard Cite

In this paper, an approach based on the U statistic is first proposed to eliminate the effect of between‐profile autocorrelation of error terms in Phase‐II monitoring of general linear profiles. Then, a control chart based on the adjusted parameter estimates is designed to monitor the parameters of the model. The performance of the proposed method is compared with the ones of some existing methods in terms of average run length for weak, moderate, and strong autocorrelation coefficients under different shift scenarios. The results show that the proposed method provides significantly better results than the competing methods to detect shifts in the regression parameters, while the competing methods perform better in detecting shifts in the standard deviation. At the end, the applicability of the proposed method is illustrated by an example. Copyright © 2015 John Wiley & Sons, Ltd.

show abstract

Statistical Monitoring of Autocorrelated Simple Linear Profiles Based on Principal Components Analysis

Cited by 17 publications

References 30 publications

Robust Holt-Winter Based Control Chart for Monitoring Autocorrelated Simple Linear Profiles With Contaminated Data

Robust Holt-Winter Based Control Chart for Monitoring Autocorrelated Simple Linear Profiles With Contaminated Data

Monitoring simple linear profiles in the presence of generalized autoregressive conditional heteroscedasticity

Phase II monitoring of general linear profiles in the presence of between-profile autocorrelation

Contact Info

Product

Resources

About