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Improvement of adaptive GAs and Back propagation ANNs performance in condition diagnosis of multiple bearing system using grey relational analysis


Wulandhari, Lili A. and Wibowo, Antoni and Desa, Mohammad I. (2014) Improvement of adaptive GAs and Back propagation ANNs performance in condition diagnosis of multiple bearing system using grey relational analysis. Computational Intelligence and Neuroscience, 2014. pp. 1-11. ISSN 1687-5265

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Abstract

Condition diagnosis of multiple bearings system is one of the requirements in industry field, because bearings are used in many equipment and their failure can result in total breakdown. Conditions of bearings commonly are reflected by vibration signals data.In multiple bearing condition diagnosis, it will involve many types of vibration signals data; thus, consequently, it will involve many features extraction to obtain precise condition diagnosis.However, large number of features extraction will increase the complexity of the diagnosis system.Therefore, in this paper, we presented a diagnosis method which is hybridization of adaptive genetic algorithms (AGAs), back propagation neural networks (BPNNs), and grey relational analysis (GRA) to diagnose the condition of multiple bearings system. AGAs are used in the diagnosis algorithm to determine the best initial weights of BPNNs in order to improve the diagnosis accuracy.In addition, GRA is applied to determine and select the dominant features from the vibration signal data which will provide good diagnosis of multiple bearings system in less features extraction.The experiments results show that AGAs-BPNNs with GRA approaches can increase the accuracy of diagnosis in shorter processing time, compared with the AGAs-BPNNs without the GRA.

Item Type: Article
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: School of Quantitative Sciences
Depositing User: Dr. Antoni Wibowo
Date Deposited: 14 Nov 2016 06:17
Last Modified: 14 Nov 2016 06:17
URI: http://repo.uum.edu.my/id/eprint/19427

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