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Hybrid intelligence failure analysis for industry 4.0: a literature review and future prospective

Author

Listed:
  • Mahdi Mokhtarzadeh

    (Ghent University
    FlandersMake@UGent–corelab ISyE)

  • Jorge Rodríguez-Echeverría

    (Ghent University
    FlandersMake@UGent–corelab ISyE
    ESPOL Polytechnic University)

  • Ivana Semanjski

    (Ghent University
    FlandersMake@UGent–corelab ISyE)

  • Sidharta Gautama

    (Ghent University
    FlandersMake@UGent–corelab ISyE)

Abstract

Industry 4.0 and advanced technology, such as sensors and human–machine cooperation, provide new possibilities for infusing intelligence into failure analysis. Failure analysis is the process of identifying (potential) failures and determining their causes and effects to enhance reliability and manufacturing quality. Proactive methodologies, such as failure mode and effects analysis (FMEA), and reactive methodologies, such as root cause analysis (RCA) and fault tree analysis (FTA), are used to analyze failures before and after their occurrence. This paper focused on failure analysis methodologies intelligentization literature applied to FMEA, RCA, and FTA to provide insights into expert-driven, data-driven, and hybrid intelligence failure analysis advancements. Types of data to establish an intelligence failure analysis, tools to find a failure’s causes and effects, e.g., Bayesian networks, and managerial insights are discussed. This literature review, along with the analyses within it, assists failure and quality analysts in developing effective hybrid intelligence failure analysis methodologies that leverage the strengths of both proactive and reactive methods.

Suggested Citation

  • Mahdi Mokhtarzadeh & Jorge Rodríguez-Echeverría & Ivana Semanjski & Sidharta Gautama, 2025. "Hybrid intelligence failure analysis for industry 4.0: a literature review and future prospective," Journal of Intelligent Manufacturing, Springer, vol. 36(4), pages 2309-2334, April.
    • Handle: RePEc:spr:joinma:v:36:y:2025:i:4:d:10.1007_s10845-024-02376-5
    DOI: 10.1007/s10845-024-02376-5
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