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

Hamzeh Soltanali,José Torres Farinha,Mehdi Khojastehpour,José Edmundo De Almeida E Pais

doi:10.3390/su14031083

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.

Highlights

With the increasing automation and development of smart technologies in modern food industries, the higher guarantee of functional safety and reliability is poised to be the major challenge towards sustainable food production [1,2,3]
The rank value of the support vector machine (SVM) algorithm overlaps fairly well with the rank value of the classic model for most failure modes with that of other intelligent models. The error indices such as mean absolute percentage error (MAPE) for fuzzy inference systems (FIS), adaptive neuro-fuzzy inference systems (ANFIS), and SVM were obtained as 21%, 4.64%, and 3.02%, respectively, and the values for root mean squ error (RMSE) were equal to 5.73, 2.85, and 1.12, respectively, to predict the classical rank value
It can be concluded that the SVM-failure mode and effect analysis (FMEA) model has a great potential for ranking all failure modes accurately with the lowest errors compared to other intelligent models

Summary

Introduction

With the increasing automation and development of smart technologies in modern food industries, the higher guarantee of functional safety and reliability is poised to be the major challenge towards sustainable food production [1,2,3]. In this context, the intelligent platforms provide the hardware and software solutions for process control and safety management within many food manufacturing systems [4,5]. The novel methods are mainly classified into the knowledge-based and data-driven approaches for risk and reliability analysis and prediction under various situations [11,12,13].

Objectives

Methods

Results

Conclusion