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New statistical and machine learning based control charts with variable parameters for monitoring generalized linear model profiles

Sabahno, Hamed ; Amiri, Amirhossein

Computers & industrial engineering, 2023-10, Vol.184, p.109562, Article 109562 [Periódico revisado por pares]

Elsevier Ltd

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  • Título:
    New statistical and machine learning based control charts with variable parameters for monitoring generalized linear model profiles
  • Autor: Sabahno, Hamed ; Amiri, Amirhossein
  • Assuntos: Computer Science ; data science ; datalogi ; Generalized Linear Models ; Machine Learning Techniques ; matematik ; Mathematics ; Monte Carlo Simulation ; Profile Monitoring ; Statistical Process Monitoring ; Statistics ; statistik ; systemanalys ; Systems Analysis ; Variable Parameters Control Charts
  • É parte de: Computers & industrial engineering, 2023-10, Vol.184, p.109562, Article 109562
  • Descrição: •Six different control charts for monitoring GLM profiles.•A new adaptive scheme for monitoring GLM profiles.•Machine learning techniques for profile monitoring.•A new algorithm to compute performance measures.•Applicability of the developed algorithms to any other control chart. In this research, we develop three statistical based control charts: the Hotelling’s T2, MEWMA (multivariate exponentially weighted moving average), and LRT (likelihood ratio test) as well as three machine learning (ML) based control charts: the ANN (artificial neural network), SVR (support vector regression), and RFR (random forest regression), for monitoring generalized linear model (GLM) profiles. We train these ML models with two different training methods to get a linear (regression) output and then apply our classification technique to see if the process is in-control or out-of-control, at each sampling time. In addition to developing the FP (fixed parameters) schemes, for the first time in GLM profiles, we design an adaptive VP (variable parameters) scheme for each control chart as well to increase the charts’ sensitivity in detecting shifts. We develop some algorithms with which the values of the control chart parameters in both FP and VP schemes can be obtained. Then, we develop two algorithms to measure the charts’ performance in both FP and VP schemes, by using the run-length and time-to-signal based performance measures. This is also the first control chart-related research that develops an algorithm to compute the performance measures that applies to any VP adaptive control scheme. After designing the control charts as well as performance measures, we perform extensive simulation studies and evaluate and compare all our control charts under different shift sizes and scenarios, and in three different simulation environments. Finally, we present a numerical example regarding a drug dose-response study to show how the proposed control charts can be implemented in real practice.
  • Editor: Elsevier Ltd
  • Idioma: Inglês
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  • Realização: ABCD-USP USP   Logos de Redes Sociais: Freepik

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