Enhancing the detection ability of control charts in profile monitoring by adding RBF ensemble model

Yeganeh, Ali; Shadman, Alireza

doi:10.1007/s00521-022-06962-7

Cited by 3 publications

(4 citation statements)

References 59 publications

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“…Relations ( 10) and (12) show that the mean function is constant over time and the covariance between 𝜖 𝑡𝑗 and 𝜖 𝑘𝑗 depends on time only through the time lag |𝑘 − 𝑡| and not otherwise on the actual times 𝑡 and 𝑘. Consequently, it can be said that the necessary and sufficient condition for the weakly stationarity of the time series 𝜀 𝑖𝑗 in Equation ( 5) is that |𝜌| < 1. From relation (12), it can be said that, since |𝜌| < 1, the autocovariance function decreases exponentially with |𝑘 − 𝑡|. In addition, covariance values are positive when 0 < 𝜌 < 1.…”

Section: Slp With a General Error Structurementioning

confidence: 99%

“…In the direct method, a process is monitored by a certain distribution of single or multiple correlated quality characteristics, while the aim of the second (indirect) method is checking the stability of a functional relationship between a dependent (response) and independent (explanatory) variable over time which is called profile monitoring . In other words, SPC methods are employed to check the stability of the functional relationship over time, which is referred to as profile monitoring in the literature, instead of monitoring a single quality characteristic 9–12 …”

Section: Introductionmentioning

confidence: 99%

“…In other words, SPC methods are employed to check the stability of the functional relationship over time, which is referred to as profile monitoring in the literature, instead of monitoring a single quality characteristic. [9][10][11][12] As a conceptual example, suppose it is necessary to control two characteristics (e.g., temperature and pressure) of an industrial process. In direct monitoring techniques, two separate univariate control charts or one multivariate control chart can be applied.…”

Section: Introductionmentioning

confidence: 99%

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Monitoring simple linear profiles in the presence of within‐ and between‐profile autocorrelation

Nadi

Yeganeh

Shadman

2023

Quality & Reliability Eng

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The current study investigates the effect of within-profile and between-profile autocorrelations on the performance of four monitoring methods of simple linear profiles in Phase II. To this end, a general correlation model between error terms is considered such that the correlation structure of within-profiles errors and the error terms between consecutive profiles follow an autoregressive (AR) times series model of order one. Extensive simulations have been done to assess the effect of both autocorrelation types and the profile size on the estimations of the model parameters as well as the performance of control charts. The performance of the monitoring schemes is investigated and compared with respect to the average run length (ARL) metric. The simulation study results show that the autocorrelation within and between profiles has a negative impact on the monitoring technique's performance. It reduces the control chart's ability to detect process shifts compared to no or weak autocorrelation cases. Moreover, the performance of all the methods is improved by increasing the profile size. An illustrative example is also provided to demonstrate the use of the proposed methods for monitoring the stability of profiles in the chemical industry.

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Section: Slp With a General Error Structurementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Monitoring simple linear profiles in the presence of within‐ and between‐profile autocorrelation

Nadi

Yeganeh

Shadman

2023

Quality & Reliability Eng

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“…Profile monitoring is usually referred to monitoring of such a regression relationship, that is, investigating the validation of the regression model over time is the main aim 6 . In this subject, different profile shapes (types) such as linear 7 – 11 , non-linear 12 , 13 , logistic 14 – 16 , Poisson 17 , 18 , circular and cylindrical 19 , mixture profiles 20 and so forth, have been extended in the SPC field. Since the idea of this paper has been adopted from Phase II monitoring of non-parametric profiles; hence, a short literature review of these types of control charts only has been provided below.…”

Section: Introductionmentioning

confidence: 99%

Monitoring non-parametric profiles using adaptive EWMA control chart

Yeganeh

Shongwe

2022

Sci Rep

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To monitor the quality of a process in statistical process control (SPC), considering a functional relationship between a dependent variable and one or more independent variables (which is denoted as profile monitoring) is becoming an increasingly common approach. Most of the studies in the SPC literature considered parametric approaches in which the functional relationship has the same form in the in-control (IC) and out-of-control (OC) situations. Non-parametric profiles, which have a different functional relationship in the OC conditions are very common. This paper designs a novel control chart to monitor not only the regression parameters but also the variation of the profiles in Phase II applications using an adaptive approach. Adaptive control charts adjust the final statistic with regard to information of the previous samples. The proposed method considers the relative distance of the chart statistic to the control limits as a tendency index and provides some outcomes about the process condition. The results of Monte Carlo simulations show the superiority of the proposed monitoring scheme in comparison with the common non-parametric control charts.

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