An improved risk and reliability framework-based maintenance planning for food processing systems

ABSTRACT

In food processing systems, it is necessary to evaluate the risk process and reliability in order to prevent unexpected events and control functional safety through well-planned maintenance. This study examines risk and reliability models and develops their theories using a novel framework based on failure behavior trends. The proposed framework was implemented in edible oil industries, specifically purification processes, and consists of three steps: first, identifying potential failures using risk-based approaches such as Failure Mode and Effect Analysis (FMEA) and Fault Tree Analysis (FTA); second, adapting a statistical structure consisting of homogenization survey and validity of identity and independence; and finally, estimating the failure rate and reliability to find the suitable maintenance intervals. The findings revealed that the proposed framework is capable of identifying critical equipment with high failure rates as the most important bottlenecks in edible oil processing lines, allowing maintenance plans to be carried out based on the various levels of reliability. Furthermore, opportunistic maintenance intervals were suggested due to the series configuration of equipment in such a process. As a result, the findings of this study may be useful in improving process safety and availability in the food processing industry.

KEYWORDS:

• Food industry
• risk
• maintenance
• reliability
• statistical based approach

Acknowledgments

The authors gratefully acknowledge the financial support from Ferdowsi University of Mashhad (FUM), Mashhad, Iran, and the Varamin oil purification plant, Iran (Project No.FUM-52316), which has been a joint Postdoctoral research project.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported by the Ferdowsi University of Mashhad [No.FUM-52316]; Ferdowsi University of Mashhad [No.FUM-52316].

Notes on contributors

Hamzeh Soltanali

Hamzeh Soltanali is a postdoctoral researcher at Ferdowsi University of Mashhad (FUM), Iran. He received his Ph.D. degree in reliability and maintenance engineering from FUM in early 2020. He was a Ph.D. visiting researcher at Luleå Tekniska Universitet (LTU), Division of Operation and Maintenance, Sweden during 2017-2018. His research focuses on maintenance engineering, asset management, smart technologies, risk and failure analysis, and reliability (RAMS) engineering. He is passionate about utilizing uncertainty qualification and AI/machine learning techniques to overcome uncertainty and variability issues in such areas.

Mehdi Khojastehpour

Mehdi Khojastehpour received his Ph.D. in Bioproduction Environmental Science from Kyushu University, Japan in 2004. He was a postdoctoral researcher in the Department of Mechanical Engineering Sciences, Kyushu University, 2004-2006. He has been an Assistant Professor, 2006-2013, Associate Professor, 2013-2020, and Full Professor 2020 as an academic member of the Department of Biosystems Engineering, Ferdowsi University of Mashhad (FUM), Iran. His research interests include Solid Mechanics and its application for biomaterials, and modeling in Postharvest Technology and Biosystems.

José Torres Farinha

José Torres Farinha has a Habilitation in Electrical Engineering and Computers, Ph.D. in Mechanical Engineering, and BSc in Electrical Engineering. He is currently Full Professor at the Instituto Superior de Engenharia de Coimbra, Portugal. His main scientific interest is Asset Management and related matters, namely Industrial Maintenance. He has three books published and almost two hundred papers and communications. He is a member of the Centre for Mechanical Engineering, Materials and Processes - CEMMPRE (Research Unit Nº 285 of the Portuguese Foundation for Science and Technology).