IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v14y2022i3p1083-d727492.html
   My bibliography  Save this article

Sustainable Food Production: An Intelligent Fault Diagnosis Framework for Analyzing the Risk of Critical Processes

Author

Listed:
  • Hamzeh Soltanali

    (Department of Biosystems Engineering, Ferdowsi University of Mashhad, Mashhad 9177948974, Iran
    Centre for Mechanical Engineering, Materials, and Processes (CEMMPRE), 3030-199 Coimbra, Portugal)

  • Mehdi Khojastehpour

    (Department of Biosystems Engineering, Ferdowsi University of Mashhad, Mashhad 9177948974, Iran)

  • José Edmundo de Almeida e Pais

    (CISE—Electromechatronic Systems Research Centre, University of Beira Interior, 6201-001 Covilhã, Portugal
    EIGeS—Research Centre in Industrial Engineering, Management and Sustainability, Lusófona University, Campo Grande 376, 1749-024 Lisboa, Portugal)

  • José Torres Farinha

    (Centre for Mechanical Engineering, Materials, and Processes (CEMMPRE), 3030-199 Coimbra, Portugal
    Coimbra Institute of Engineering, Polytechnic of Coimbra, 3030-199 Coimbra, Portugal)

Abstract

Fault diagnosis and prognosis methods are the most useful tools for risk and reliability analysis in food processing systems. Proactive diagnosis techniques such as failure mode and effect analysis (FMEA) are important for detecting all probable failures and facilitating the risk analysis process. However, significant uncertainties exist in the classical-FMEA when it comes to ranking the risk priority numbers (RPNs) of failure modes. Such uncertainties may have an impact on the food sector’s operational safety and maintenance decisions. To address these issues, this research provides a unique FMEA framework for risk analysis within an edible oil purification facility that is based on certain well-known intelligent models. Fuzzy inference systems (FIS), adaptive neuro-fuzzy inference systems (ANFIS), and support vector machine (SVM) models are among those used. The findings of the comparison of the proposed FMEA framework with the classical model revealed that intelligent strategies were more effective in ranking the RPNs of failure modes. Based on the performance criteria, it was discovered that the SVM algorithm classifies the failure modes more accurately and with fewer errors., e.g., RMSE = 7.30 and MAPE = 13.19 with that of other intelligent techniques. Hence, a sensitivity FMEA analysis based on the SVM algorithm was performed to put forward suitable maintenance actions to upgrade the reliability and safety within food processing lines.

Suggested Citation

  • Hamzeh Soltanali & Mehdi Khojastehpour & José Edmundo de Almeida e Pais & José Torres Farinha, 2022. "Sustainable Food Production: An Intelligent Fault Diagnosis Framework for Analyzing the Risk of Critical Processes," Sustainability, MDPI, vol. 14(3), pages 1-22, January.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:3:p:1083-:d:727492
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/14/3/1083/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/14/3/1083/
    Download Restriction: no
    ---><---

    Citations

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


    Cited by:

    1. Hamzeh Soltanali & Mehdi Khojastehpour & Siamak Kheybari, 2023. "Evaluating the critical success factors for maintenance management in agro-industries using multi-criteria decision-making techniques," Operations Management Research, Springer, vol. 16(2), pages 949-968, June.
    2. Andrés A. Zúñiga & João F. P. Fernandes & Paulo J. C. Branco, 2023. "Fuzzy-Based Failure Modes, Effects, and Criticality Analysis Applied to Cyber-Power Grids," Energies, MDPI, vol. 16(8), pages 1-34, April.
    3. Alexandre Martins & Balduíno Mateus & Inácio Fonseca & José Torres Farinha & João Rodrigues & Mateus Mendes & António Marques Cardoso, 2023. "Predicting the Health Status of a Pulp Press Based on Deep Neural Networks and Hidden Markov Models," Energies, MDPI, vol. 16(6), pages 1-26, March.
    4. Li, Ying & Liu, Peide & Li, Gang, 2023. "An asymmetric cost consensus based failure mode and effect analysis method with personalized risk attitude information," Reliability Engineering and System Safety, Elsevier, vol. 235(C).
    5. Özlem Arslan & Necip Karakurt & Ecem Cem & Selcuk Cebi, 2023. "Risk Analysis in the Food Cold Chain Using Decomposed Fuzzy Set-Based FMEA Approach," Sustainability, MDPI, vol. 15(17), pages 1-20, September.

    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:gam:jsusta:v:14:y:2022:i:3:p:1083-:d:727492. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.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.