Prognosis of failures due to the abnormal temperature increase of the malt crusher, using ANFIS neuro-fuzzy approach: case study of the flour mill of the breweries of Cameroon

Timothee Kombe and Sandra Nzeneu

International Journal of Industrial and Systems Engineering, 2022, vol. 40, issue 2, 228-254

Abstract: In this article, we present a method for predicting malt grinder failures. The objective is to control the evolution of the temperature of the grinding chamber, in order to optimise the availability and reliability of the grinder. The methodological approach is based on the ANFIS neuro-fuzzy network, which offers, in a single tool, precision for nonlinear systems. The predicted temperature is classified according to a mode of operation of the equipment. The evaluation of the performance of the prediction and classification systems is characterised respectively by a learning error function of 0.1236 and a classification rate of 79.6%.

Keywords: artificial intelligence; hybrid neuro-fuzzy network; form recognition; failure; prognosis; Cameroon. (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:ids:ijisen:v:40:y:2022:i:2:p:228-254

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