Classification of Belts Status Based on an Automatic Generator of Fuzzy Rules Base System
Source abstract
Abstract
The automation of maintenance is a growing field and consequently, predictive maintenance is achieving more importance. The main objective is to predict a breakage before it happens. In order to reach this, it is necessary to have an intelligent classification technique that analyzes the state of the key breakage elements and evaluates whether a replacement is necessary or not. This work presents a study to classify belts according to their state of use. For training, vibration data have been collected on a test bench using new belts, belts with half use and belts near the breaking point. The processing of these vibrations allows for extracting the characteristic parameters that can be related to its state of use, and then, after the initial analysis, these values are used as inputs for training the intelligent system. In particular, the Genetic Neuro-Fuzzy (GNF) technique has been chosen and, with the proposed algorithm, more detailed Fuzzy rules are obtained. Once the algorithm has been trained, it is possible to establish a relationship between the vibration shown by the belt and its state of use. The achieved results show that a good classifier has been built.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Belt Conveyor Systems Engineering · Tunneling and Rock Mechanics · Industrial Automation and Control Systems
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Escuela Superior de Ingeniería y Tecnología, Universidad de La Laguna, 38200 Tenerife, Spain
E.T.S. de Ingenieros Industriales, Universidad Nacional de Educación a Distancia, 28040 Madrid, Spain
Funding
Programa de I+D+I orientada a los retos de la Sociedad, Proyecto Nacional · Programa INTERREGMAC 2014-2020 de la Unión Europea, parte del Proyecto E5DES
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