Fault diagnosis of flexible production line machining center based on PCA and ABC-LVQ

Xia, Y.; Wang, W.; Song, Z.; Xie, Z.; Chen, X.; Li, H.; Xia, Y; Wang, W; Song, Z; Xie, Z; Chen, X; Li, H

doi:10.1177/0954405420970513

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Fault diagnosis of flexible production line machining center based on PCA and ABC-LVQ

Xia, Y.; Wang, W.; Song, Z.; Xie, Z.; Chen, X.; Li, H.

Proceedings of the Institution of Mechanical Engineers, Part B Journal of Engineering Manufacture 235(4): 594-604

2020

ISSN/ISBN: 0954-4054

DOI: 10.1177/0954405420970513
Accession: 084002524

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Summary

A fault diagnosis method of flexible production line machining center based on principal component analysis (PCA) and artificial bee colony based learning vector quantization (ABC-LVQ) neural network is proposed to target the issue of key vulnerable parts of the flexible production line machining center. Along with that, a general scheme of fault diagnosis is designed according to the characteristics of flexible production line which includes multiple machining centers. Firstly, the acceleration sensor is used to collect the vibration signals of the key and critical components of the flexible production line machining center, to analyze and pick up the multi-information characteristics, the time domain, the frequency domain and the time-frequency domain of the signals. Then, in order to reduce the dimension of the eigenvector, PCA is adopted to realize such reduction. Finally, aiming at the problem that LVQ neural network is sensitive to the initial value weights, an ABC algorithm is proposed to quickly obtain excellent initial weights vector of LVQ neural network, which can accurately classify all kinds of faults. The experimental results show that the scheme can diagnose and monitor several key components of horizontal machining center of flexible production line in a quick and accurate manner.

Fault diagnosis of flexible production line machining center based on PCA and ABC-LVQ

Xia, Y.; Wang, W.; Song, Z.; Xie, Z.; Chen, X.; Li, H.

Proceedings of the Institution of Mechanical Engineers, Part B Journal of Engineering Manufacture 235(4): 594-604

2020

Full-Text Article emailed within 0-6 h

Summary

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