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

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Abstract

While numerous contributions and applications have been extended in profile monitoring, little attention has been paid to employing machine learning techniques in development of control charts. In this paper, a novel control chart based on artificial neural network is proposed to improve the performance of monitoring general linear profiles in Phase II. Specifically, an ensemble of radial basis functions (RBF) is added to the predefined base control chart to enhance the detection ability of the control chart for monitoring linear profile parameters based on the average run length (ARL) criterion. The performance of the proposed method is evaluated by adjusting the multivariate exponentially weighted moving average (MEWMA) control chart as a base control chart under simple and multiple linear profiles. The simulation results demonstrate that the proposed approach is very efficient than competing existing methods for monitoring linear profile parameters. Moreover, profile diagnosis actions, referring to the identification of shifted parameters, are provided based on the RBF networks. Finally, we provide an example from thermal management to illustrate the implementation of the proposed monitoring scheme and diagnostic method.

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Funding

This research is supported by Ferdowsi University of Mashhad, under Grant No. 51697.

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Authors and Affiliations

  1. Department of Industrial Engineering, Faculty of Engineering, Ferdowsi University of Mashhad, Mashhad, Iran

    Ali Yeganeh & Alireza Shadman

  2. Statistics Program, Department of Mathematics, Statistics and Physics, College of Arts and Science, Qatar University, 2713, Doha, Qatar

    Saddam Akber Abbasi

  3. Statistical Consulting Unit, Department of Mathematics, Statistics and Physics, College of Arts and Science, Qatar University, 2713, Doha, Qatar

    Saddam Akber Abbasi

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  1. Ali Yeganeh
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  2. Alireza Shadman
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Correspondence to Alireza Shadman.

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Yeganeh, A., Shadman, A. & Abbasi, S.A. Enhancing the detection ability of control charts in profile monitoring by adding RBF ensemble model. Neural Comput & Applic 34, 9733–9757 (2022). https://doi.org/10.1007/s00521-022-06962-7

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