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Diagnosis of the Failures of a Complex Industrial System by Neuro-Fuzzy Networks Optimized by a Genetic Algorithm (ANFIS-GA): A Case of the Franceville Brewery

Diagnosis of the Failures of a Complex Industrial System by Neuro-Fuzzy Networks Optimized by a Genetic Algorithm (ANFIS-GA): A Case of the Franceville Brewery
	© 2023 by IJETT Journal
	Volume-71 Issue-2
	Year of Publication : 2023
	Author : Ondo Boniface, Nyatte Steyve, Kombé Thimotée, Elé Perre
	DOI : 10.14445/22315381/IJETT-V71I2P227

How to Cite?

Ondo Boniface, Nyatte Steyve, Kombé Thimotée, Elé Perre, "Diagnosis of the Failures of a Complex Industrial System by Neuro-Fuzzy Networks Optimized by a Genetic Algorithm (ANFIS-GA): A Case of the Franceville Brewery," International Journal of Engineering Trends and Technology, vol. 71, no. 2, pp. 236-248, 2023. Crossref, https://doi.org/10.14445/22315381/IJETT-V71I2P227

Abstract
Fault diagnosis in intricate industrial operations is a difficult task, especially in the African context, due to the stochastic interaction between symptoms and faults, a lot of inputs and outputs, and the difficulty of acquiring characteristic data (spectral study, sound, vibration, electrical quantities, etc.) of the operating state through specialized sensors. Furthermore, if this diagnosis is performed online, a fast time algorithm is required to account for the system's instantaneous changes. With the objective of reducing maintenance costs, improving productivity, and increasing machine availability, we develop an online fault diagnosis model for a dynamic process based on an adaptive neuro-fuzzy inference system (ANFIS) based on the production history and associated faults. This algorithm is optimized by the algorithm based on gene (GA) to learn the defect-production correlation of a brewery from historical production and process failure data. This method, based on the data, such as the format of bottles produced, daily production hours, number of manufactured bottles without defects per day, number of manufactured bottles with defects per day, and downtime of production subsystems, allows us to extract the data-driven defect-symptom correlation. Optimizing an ANFIS classifier for fault diagnosis reduces the computation time and increases accuracy, thus allowing the integration of newly identified faults in the process. In conclusion, the proposed model, based on GA-ANFIS, is tested on the process of the Franceville brewery in Gabon. The results on our dataset are better than other types of data from some studies according to their accuracy (88.97%), precision (89.23%), sensitivity (73.20%), and specificity (96.27%).

Keywords
ANFIS, Complex industrial system, Diagnosis of the failures, Reliability, Optimization.

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