OPTIMIZATION OF EQUIPMENT RELIABILITY BASED ON A NEURO-FUZZY APPROACH: CASE OF A FLOUR MILL

The main objective of this paper is to present an innovative approach combining fuzzy logic and artificial neural networks to optimize equipment reliability in the specific context of a flour mill. Faced with the challenges of performance and profitability in this industrial sector, the neuro-fuzzy methodology has been developed to meet the challenges related to the complexity and uncertainty inherent in equipment reliability management. The first part of the paper provides an overview of the problem, introducing the key concepts of reliability and maintenance, while highlighting the particular challenges of the milling industry. This paper also outlines the advantages of the neurofuzzy approach for optimizing equipment reliability. The methodology for developing the neuro-fuzzy model is detailed in the second part. It covers the construction of the fuzzy inference system, the design of the neural network structure, as well as the training and optimization steps of the model. The case study conducted in a flour mill is presented in the third part. After a description of the company and its equipment system, the collection and analysis of reliability data are presented, as well as the implementation of the developed neuro-fuzzy model. The results obtained demonstrate that this methodology makes it possible to better anticipate failures, optimize maintenance interventions, and reduce associated costs. Sensitivity analysis and comparison with other optimization methods confirm the validity and operational and economic benefits of the proposed approach.