Condition-based monitoring system for rolling element bearing using a generic multi-layer perceptron
Rolling element bearings are critical mechanical components in rotating machinery and fault detection in the early stages of damage is important to prevent their malfunctioning and failure. Vibration monitoring is the most widely used and cost-effective monitoring technique to detect, locate and dis...
| Autores: | , , , |
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| Formato: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2015 |
| País: | Brasil |
| Recursos: | Universidade Estadual Paulista (UNESP) |
| Repositorio: | Repositório Institucional da UNESP |
| Idioma: | inglés |
| OAI Identifier: | oai:repositorio.unesp.br:11449/158609 |
| Acesso em linha: | http://dx.doi.org/10.1177/1077546314524260 http://hdl.handle.net/11449/158609 |
| Access Level: | acceso abierto |
| Palavra-chave: | Artificial Neural Network Multi Layer Perceptron Condition-Based Monitoring vibration monitoring |
| Resumo: | Rolling element bearings are critical mechanical components in rotating machinery and fault detection in the early stages of damage is important to prevent their malfunctioning and failure. Vibration monitoring is the most widely used and cost-effective monitoring technique to detect, locate and distinguish faults in rolling element bearings. This paper purposes single hidden layer architecture for fault diagnosis of rolling element bearings. The particular of this proposed architecture is its ability to generalize for solving both basic classification and fault identification. The network uses the features of time-domain vibration signals with normal and defective bearings. The Multi Layer Perceptron (MLP) was trained and tested with a set of experimental data obtained from previous experiments developed by FEG, CWRU and RANDALL laboratories. The results show the effectiveness of the MLP to diagnose the machine condition for the various data used. |
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