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New machine-learning control charts for simultaneous monitoring of multivariate normal process parameters with detection and identification

Sabahno, Hamed (author)
Umeå universitet,Statistik
Niaki, Seyed Taghi Akhavan (author)
Department of Industrial Engineering, Sharif University of Technology, Tehran, Iran
 (creator_code:org_t)
MDPI, 2023
2023
English.
In: Mathematics. - : MDPI. - 2227-7390. ; 11:16
  • Journal article (peer-reviewed)
Abstract Subject headings
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  • Simultaneous monitoring of the process parameters in a multivariate normal process has caught researchers’ attention during the last two decades. However, only statistical control charts have been developed so far for this purpose. On the other hand, machine-learning (ML) techniques have rarely been developed to be used in control charts. In this paper, three ML control charts are proposed using the concepts of artificial neural networks, support vector machines, and random forests techniques. These ML techniques are trained to obtain linear outputs, and then based on the concepts of memory-less control charts, the process is classified into in-control or out-of-control states. Two different input scenarios and two different training methods are used for the proposed ML structures. In addition, two different process control scenarios are utilized. In one, the goal is only the detection of the out-of-control situation. In the other one, the identification of the responsible variable (s)/process parameter (s) for the out-of-control signal is also an aim (detection–identification). After developing the ML control charts for each scenario, we compare them to one another, as well as to the most recently developed statistical control charts. The results show significantly better performance of the proposed ML control charts against the traditional memory-less statistical control charts in most compared cases. Finally, an illustrative example is presented to show how the proposed scheme can be implemented in a healthcare process.

Subject headings

TEKNIK OCH TEKNOLOGIER  -- Maskinteknik -- Tillförlitlighets- och kvalitetsteknik (hsv//swe)
ENGINEERING AND TECHNOLOGY  -- Mechanical Engineering -- Reliability and Maintenance (hsv//eng)
NATURVETENSKAP  -- Matematik -- Sannolikhetsteori och statistik (hsv//swe)
NATURAL SCIENCES  -- Mathematics -- Probability Theory and Statistics (hsv//eng)
NATURVETENSKAP  -- Matematik -- Beräkningsmatematik (hsv//swe)
NATURAL SCIENCES  -- Mathematics -- Computational Mathematics (hsv//eng)

Keyword

process monitoring; machine-learning techniques; simultaneous process parameters monitoring; multivariate normal process; simulation
Mathematics
matematik
Statistics
statistik
data science
data science

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art (subject category)

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Sabahno, Hamed
Niaki, Seyed Tag ...
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ENGINEERING AND TECHNOLOGY
ENGINEERING AND ...
and Mechanical Engin ...
and Reliability and ...
NATURAL SCIENCES
NATURAL SCIENCES
and Mathematics
and Probability Theo ...
NATURAL SCIENCES
NATURAL SCIENCES
and Mathematics
and Computational Ma ...
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Mathematics
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Umeå University

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