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

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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.
Keywords
Automated failure analysis, Data-driven failure analysis, FTA, FMECA, Human–machine cooperation, RCA, Human-machine cooperation, ROOT CAUSE ANALYSIS, OF-THE-ART, BAYESIAN-NETWORK, FAULT-DIAGNOSIS, RELIABILITY-ANALYSIS, MONITORING-SYSTEM, RISK ANALYSIS, SAFETY, MODES, GENERATION

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MLA
Mokhtarzadeh, Mahdi, et al. “Hybrid Intelligence Failure Analysis for Industry 4.0 : A Literature Review and Future Prospective.” JOURNAL OF INTELLIGENT MANUFACTURING, 2024, doi:10.1007/s10845-024-02376-5.
APA
Mokhtarzadeh, M., Rodriguez Echeverría, J., Semanjski, I., & Gautama, S. (2024). Hybrid intelligence failure analysis for industry 4.0 : a literature review and future prospective. JOURNAL OF INTELLIGENT MANUFACTURING. https://doi.org/10.1007/s10845-024-02376-5
Chicago author-date
Mokhtarzadeh, Mahdi, Jorge Rodriguez Echeverría, Ivana Semanjski, and Sidharta Gautama. 2024. “Hybrid Intelligence Failure Analysis for Industry 4.0 : A Literature Review and Future Prospective.” JOURNAL OF INTELLIGENT MANUFACTURING. https://doi.org/10.1007/s10845-024-02376-5.
Chicago author-date (all authors)
Mokhtarzadeh, Mahdi, Jorge Rodriguez Echeverría, Ivana Semanjski, and Sidharta Gautama. 2024. “Hybrid Intelligence Failure Analysis for Industry 4.0 : A Literature Review and Future Prospective.” JOURNAL OF INTELLIGENT MANUFACTURING. doi:10.1007/s10845-024-02376-5.
Vancouver
1.
Mokhtarzadeh M, Rodriguez Echeverría J, Semanjski I, Gautama S. Hybrid intelligence failure analysis for industry 4.0 : a literature review and future prospective. JOURNAL OF INTELLIGENT MANUFACTURING. 2024;
IEEE
[1]
M. Mokhtarzadeh, J. Rodriguez Echeverría, I. Semanjski, and S. Gautama, “Hybrid intelligence failure analysis for industry 4.0 : a literature review and future prospective,” JOURNAL OF INTELLIGENT MANUFACTURING, 2024.
@article{01HWPW2F0PDT7Z9QY63NGC3HTK,
  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.}},
  author       = {{Mokhtarzadeh, Mahdi and Rodriguez Echeverría, Jorge and Semanjski, Ivana and Gautama, Sidharta}},
  issn         = {{0956-5515}},
  journal      = {{JOURNAL OF INTELLIGENT MANUFACTURING}},
  keywords     = {{Automated failure analysis,Data-driven failure analysis,FTA,FMECA,Human–machine cooperation,RCA,Human-machine cooperation,ROOT CAUSE ANALYSIS,OF-THE-ART,BAYESIAN-NETWORK,FAULT-DIAGNOSIS,RELIABILITY-ANALYSIS,MONITORING-SYSTEM,RISK ANALYSIS,SAFETY,MODES,GENERATION}},
  language     = {{eng}},
  title        = {{Hybrid intelligence failure analysis for industry 4.0 : a literature review and future prospective}},
  url          = {{http://doi.org/10.1007/s10845-024-02376-5}},
  year         = {{2024}},
}

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