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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 LA, Wibowo A, Desa MI - Comput Intell Neurosci (2014)

Bottom Line: However, large number of features extraction will increase the complexity of the diagnosis system.AGAs are used in the diagnosis algorithm to determine the best initial weights of BPNNs in order to improve the diagnosis accuracy.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.

View Article: PubMed Central - PubMed

Affiliation: School of Computer Science, Bina Nusantara University, Jakarta 11480, Indonesia.

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.

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Grey relational analysis procedures.
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fig4: Grey relational analysis procedures.

Mentions: A system for which the relevant information is completely known is called a white system, while a system for which the relevant information is completely unknown is a black system. Any system between these limits is a grey system which has poor and limited information [23]. In multiple bearing systems, any information about the condition of the bearing is not completely revealed by the features extracted from the vibration signal data. This unclear condition of data can be overcome using GRA which was proposed by Deng [24] in 1982. GRA utilizes the mathematical method to analyzing correlation between the references series which is the ideal value of features and the alternatives series [25]. It firstly normalizes the features extracted and then translates the performance of all alternatives into a comparability sequence with the ideal value called grey relational generating [26], followed by the calculation of grey relational coefficient between all comparability sequences and the references sequences. Finally, the grey relational grade between the reference sequence and every comparability sequence is calculated based on the grey relational coefficient. The highest grey relational grade of the alternatives features indicates that the features have dominant influence to the condition diagnosis. The procedures of GRA are shown in Figure 4.


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

Wulandhari LA, Wibowo A, Desa MI - Comput Intell Neurosci (2014)

Grey relational analysis procedures.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4281473&req=5

fig4: Grey relational analysis procedures.
Mentions: A system for which the relevant information is completely known is called a white system, while a system for which the relevant information is completely unknown is a black system. Any system between these limits is a grey system which has poor and limited information [23]. In multiple bearing systems, any information about the condition of the bearing is not completely revealed by the features extracted from the vibration signal data. This unclear condition of data can be overcome using GRA which was proposed by Deng [24] in 1982. GRA utilizes the mathematical method to analyzing correlation between the references series which is the ideal value of features and the alternatives series [25]. It firstly normalizes the features extracted and then translates the performance of all alternatives into a comparability sequence with the ideal value called grey relational generating [26], followed by the calculation of grey relational coefficient between all comparability sequences and the references sequences. Finally, the grey relational grade between the reference sequence and every comparability sequence is calculated based on the grey relational coefficient. The highest grey relational grade of the alternatives features indicates that the features have dominant influence to the condition diagnosis. The procedures of GRA are shown in Figure 4.

Bottom Line: However, large number of features extraction will increase the complexity of the diagnosis system.AGAs are used in the diagnosis algorithm to determine the best initial weights of BPNNs in order to improve the diagnosis accuracy.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.

View Article: PubMed Central - PubMed

Affiliation: School of Computer Science, Bina Nusantara University, Jakarta 11480, Indonesia.

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.

Show MeSH
Related in: MedlinePlus