Time-Series Modeling for Statistical Process Control

Alwan, Layth C.; Roberts, Harry V.

doi:10.2307/1391421

Cited by 329 publications

(233 citation statements)

References 0 publications

Supporting

Mentioning

221

Contrasting

Unclassified

Order By: Relevance

“…Following the minimum discrimination information principle 14,47 , at each monitoring period we use a new sequence to construct a frequency table in a similar manner to Table II and computes the new Chi-squared statistics.…”

Section: Calculation Of the Mspc Control Limitsmentioning

confidence: 99%

The funnel experiment: The Markov‐based SPC approach

Singer

Ben‐Gal

2007

Quality & Reliability Eng

View full text Add to dashboard Cite

The classical funnel experiment was used by Deming to promote the idea of statistical process control (SPC). The popular example illustrates that the implementation of simple feedback rules to stationary processes violates the independence assumption and prevents the implementation of conventional SPC. However, Deming did not indicate how to implement SPC in the presence of such feedback rules. This pedagogical gap is addressed here by introducing a simple feedback rule to the funnel example that results in a nonlinear process to which the traditional SPC methods cannot be applied. The proposed method of Markov-based SPC, which is a simplified version of the context-based SPC method, is shown to monitor the modified process well.

show abstract

Section: Calculation Of the Mspc Control Limitsmentioning

confidence: 99%

The funnel experiment: The Markov‐based SPC approach

Singer

Ben‐Gal

2007

Quality & Reliability Eng

View full text Add to dashboard Cite

show abstract

“…As many authors have pointed out (VanBrackle and Reynolds, 1997; Schmid, 1997; Psarakis and Papaleonida, 2007), this has the implication that the control limits need to be tightened in order to take into account autocorrelation. Alwan and Roberts (1988) presented a control procedure for the residuals in order to treat the effect of correlation. This control procedure monitors the residuals of an adequate model that describes the data.…”

Section: Introductionmentioning

confidence: 99%

“…This control procedure monitors the residuals of an adequate model that describes the data. Several other studies consider monitoring the residuals after fitting a time series model to processes that exhibit autocorrelation and may be stationary or non-stationary (Hawkins, 1991 Considering autocorrelated processes (either industrial or non-industrial) a key difficulty for devising control charts is the definition of the in-control state; Alwan and Roberts (1988) discuss this difficulty. Due to autocorrelation and time-series dynamics such processes may exhibit certain non-standard variation over time even in the in-control state.…”

Section: Introductionmentioning

confidence: 99%

Phase II control charts for autocorrelated processes

Triantafyllopoulos

Bersimis

2016

Quality Technology & Quantitative Management

View full text Add to dashboard Cite

A large amount of SPC procedures are based on the assumption that the process subject to monitoring consists of independent observations. Chemical processes as well as many non-industrial processes exhibit autocorrelation, for which the above mentioned control procedures are not suitable. This paper proposes a Phase II control procedure for This comparison is carried out using Bayes factors, which help to establish the in-control or out-of-control state of the process in Phase II. Using the threshold rules of the Bayes factors we propose a binomial-type control procedure for the monitoring of the process.The methodology of this paper is illustrated using two data sets consisting of temperature 1 measurements at two different stages in the manufacturing of a plastic mould.

show abstract

“…Maragah and Woodall [3] provided results on the effect of autocorrelation on the performance of the Shewhart individuals control chart. Alwan and Roberts [4] found that more than 70% of the studied processes subject to change detection in quality control are autocorrelated. Moreover, Correspondence: prajapatimed@gmail.com in practice the positive type autocorrelation is more prevalent compared to the negative type.…”

Section: Introductionmentioning

confidence: 99%

Cost comparisons of modifiedX̅chart for autocorrelated observations

Prajapati

2016

Int. J. Metrol. Qual. Eng.

View full text Add to dashboard Cite

Abstract. This paper presents the economic design of modified X chart for autocorrelated data and comparison with the economic design of Shewhart's X chart. An attempt has been made to counter autocorrelation by designing the modified X chart; as the cost of operating a process control system is an important element in the economic design of control charts. The economic designs of both modified and Shewhart X charts for autocorrelated observations are presented; using Lorenzen-Vance [1] cost model. The modified X chart is based upon sum of chi-squares and has simplicity like Shewhart X chart but more efficient than standard chart. Two sets of parameters suggested by Montgomery [2] are used to determine the optimal sampling intervals (h) and expected costs per hour. It shows that sampling interval (h) and expected cost/hour (C) depend upon various parameters of chart, used in this model. The expected costs/hour (C) for both the charts are computed for sample sizes of 2 and 4 at the various levels of correlation and process shifts in mean. It is concluded that at each level of correlation and shift in the process mean, the expected costs/hour of the modified X chart are lower than the Shewhart chart.

show abstract

Time-Series Modeling for Statistical Process Control

Cited by 329 publications

References 0 publications

The funnel experiment: The Markov‐based SPC approach

The funnel experiment: The Markov‐based SPC approach

Phase II control charts for autocorrelated processes

Cost comparisons of modifiedX̅chart for autocorrelated observations

Contact Info

Product

Resources

About