IDEAS home Printed from https://ideas.repec.org/h/spr/ssrchp/978-3-030-83819-5_2.html
   My bibliography  Save this book chapter

Application of Machine Learning in Statistical Process Control Charts: A Survey and Perspective

In: Control Charts and Machine Learning for Anomaly Detection in Manufacturing

Author

Listed:
  • Phuong Hanh Tran

    (Dong A University)

  • Adel Ahmadi Nadi

    (Ferdowsi University of Mashhad
    University of Lille, ENSAIT, GEMTEX)

  • Thi Hien Nguyen

    (Dong A University
    Laboratoire AGM, UMR CNRS 8088, CY Cergy Paris Université)

  • Kim Duc Tran

    (Dong A University)

  • Kim Phuc Tran

    (University of Lille, ENSAIT, GEMTEX)

Abstract

Over the past decades, control charts, one of the essential tools in Statistical Process Control (SPC), have been widely implemented in manufacturing industries as an effective approach for Anomaly Detection (AD). Thanks to the development of technologies like the Internet of Things (IoT) and Artificial Intelligence (AI), Smart Manufacturing (SM) has become an important concept for expressing the end goal of digitization in manufacturing. However, SM requires a more automatic procedure with capabilities to deal with huge data from the continuous and simultaneous process. Hence, traditional control charts of SPC now find difficulties in reality activities including designing, pattern recognition, and interpreting stages. Machine Learning (ML) algorithms have emerged as powerful analytic tools and great assistance that can be integrating to control charts of SPC to solve these issues. Therefore, the purpose of this chapter is first to presents a survey on the applications of ML techniques in the stages of designing, pattern recognition, and interpreting of control charts respectively in SPC especially in the context of SM for AD. Second, difficulties and challenges in these areas are discussed. Third, perspectives of ML techniques-based control charts for AD in SM are proposed. Finally, a case study of an ML-based control chart for bearing failure AD is also provided in this chapter.

Suggested Citation

  • Phuong Hanh Tran & Adel Ahmadi Nadi & Thi Hien Nguyen & Kim Duc Tran & Kim Phuc Tran, 2022. "Application of Machine Learning in Statistical Process Control Charts: A Survey and Perspective," Springer Series in Reliability Engineering, in: Kim Phuc Tran (ed.), Control Charts and Machine Learning for Anomaly Detection in Manufacturing, pages 7-42, Springer.
    • Handle: RePEc:spr:ssrchp:978-3-030-83819-5_2
    DOI: 10.1007/978-3-030-83819-5_2
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a search for a similarly titled item that would be available.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Zulfiqar Ali & Sadia Qamar & Nasrulla Khan & Muhammad Faisal & Saad Sh. Sammen, 2023. "A New Regional Drought Index under X-bar Chart Based Weighting Scheme – The Quality Boosted Regional Drought Index (QBRDI)," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(5), pages 1895-1911, March.
    2. Ethel García & Rita Peñabaena-Niebles & Maria Jubiz-Diaz & Angie Perez-Tafur, 2022. "Concurrent Control Chart Pattern Recognition: A Systematic Review," Mathematics, MDPI, vol. 10(6), pages 1-31, March.
    3. Wilson Rojas-Preciado & Mauricio Rojas-Campuzano & Purificación Galindo-Villardón & Omar Ruiz-Barzola, 2023. "Control Chart T2Qv for Statistical Control of Multivariate Processes with Qualitative Variables," Mathematics, MDPI, vol. 11(12), pages 1-32, June.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:ssrchp:978-3-030-83819-5_2. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.