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Introduction to Control Charts and Machine Learning for Anomaly Detection in Manufacturing

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

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  • Kim Phuc Tran

    (University of Lille, ENSAIT, GEMTEX)

Abstract

In this chapter, we provide an introduction to Anomaly Detection and potential applications in manufacturing using Control Charts and Machine Learning techniques. We elaborate on the peculiarities of process monitoring and Anomaly Detection with Control Charts and Machine Learning in the manufacturing process and especially in the smart manufacturing contexts. We present the main research directions in this area and summarize the structure and contribution of the book.

Suggested Citation

  • Kim Phuc Tran, 2022. "Introduction to Control Charts and Machine Learning for Anomaly Detection in Manufacturing," Springer Series in Reliability Engineering, in: Kim Phuc Tran (ed.), Control Charts and Machine Learning for Anomaly Detection in Manufacturing, pages 1-6, Springer.
    • Handle: RePEc:spr:ssrchp:978-3-030-83819-5_1
    DOI: 10.1007/978-3-030-83819-5_1
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    Cited by:

    1. 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.
    2. 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.
    3. 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.

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