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Reliability Study of an Intelligent Profiling Progressive Automatic Glue Cutter Based on the Improved FMECA Method

Author

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  • Heng Zhang

    (School of Information and Communication Engineering, Hainan University, Haikou 570228, China)

  • Yaya Chen

    (School of Information and Communication Engineering, Hainan University, Haikou 570228, China)

  • Jingyu Cong

    (School of Information and Communication Engineering, Hainan University, Haikou 570228, China
    State Key Laboratory of Marine Resource Utilization in South China Sea, Hainan University, Haikou 570228, China)

  • Junxiao Liu

    (School of Mechanical and Electrical Engineering, Hainan University, Haikou 570228, China
    Sanya Nanfan Research Institute, Hainan University, Sanya 572025, China)

  • Zhifu Zhang

    (School of Mechanical and Electrical Engineering, Hainan University, Haikou 570228, China)

  • Xirui Zhang

    (School of Information and Communication Engineering, Hainan University, Haikou 570228, China
    School of Mechanical and Electrical Engineering, Hainan University, Haikou 570228, China
    Sanya Nanfan Research Institute, Hainan University, Sanya 572025, China)

Abstract

This study introduces the fuzzy theory approach as an enhancement to the traditional failure mode, effect, and criticality analysis (FMECA) method in order to address its limitations, which primarily stem from subjectivity and a lack of quantitative analysis. The proposed method, referred to as FMECA improvement based on fuzzy comprehensive evaluation, aims to quantify the qualitative aspect of the analysis and provides a detailed outline of the analysis procedure. By applying the enhanced FMECA method to assess the reliability of an intelligent profiling progressive automatic rubber cutter, the hazard ranking for each failure mode of the cutter can be determined, thereby identifying areas that require reliability improvement. The analysis outcomes demonstrate that this method establishes a theoretical foundation for subsequent cutter improvement designs, enables early identification of potential failures, and consequently leads to a reduced failure rate and an enhanced reliability level for the intelligent profiling progressive automatic cutter. Furthermore, this innovative agricultural equipment reliability analysis and testing approach holds significant value in elevating the reliability standards of agricultural equipment as a whole and can be explored and implemented in other agricultural machinery contexts.

Suggested Citation

  • Heng Zhang & Yaya Chen & Jingyu Cong & Junxiao Liu & Zhifu Zhang & Xirui Zhang, 2023. "Reliability Study of an Intelligent Profiling Progressive Automatic Glue Cutter Based on the Improved FMECA Method," Agriculture, MDPI, vol. 13(8), pages 1-17, July.
    • Handle: RePEc:gam:jagris:v:13:y:2023:i:8:p:1475-:d:1202704
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    References listed on IDEAS

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    2. Carpitella, Silvia & Certa, Antonella & Izquierdo, Joaquín & La Fata, Concetta Manuela, 2018. "A combined multi-criteria approach to support FMECA analyses: A real-world case," Reliability Engineering and System Safety, Elsevier, vol. 169(C), pages 394-402.
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    4. Huang, Jia & Li, Zhaojun(Steven) & Liu, Hu-Chen, 2017. "New approach for failure mode and effect analysis using linguistic distribution assessments and TODIM method," Reliability Engineering and System Safety, Elsevier, vol. 167(C), pages 302-309.
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