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PHM °ü·Ã
±¹³»¿Ü ÃֽŠ´ëÇ¥ ³í¹® (Vol.4)
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Dong, H., Yang, X., Li, A., Xie, Z.
and Zuo, Y., Bio-inspired PHM model for diagnostics of faults in power transformers
using dissolved gas-in-oil data, Sensors, 19(4), pp.845_1-13, 2019.
- DOI: http://dx.doi.org/10.3390/s19040845
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Khumprom, P. and Yodo, N., A Data-driven
predictive prognostic model for lithium-ion batteries based on a deep learning
algorithm, Energies, 12(4), pp.660_1-21, 2019.
- DOI: http://dx.doi.org/10.3390/en12040660
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Wu, C., Jiang, P., Ding, C., Feng, F.
and Chen, T., Intelligent fault diagnosis of rotating machinery based on
one-dimensional convolutional neural network, Computers
in Industry, 108, pp. 53-61, 2019.
- DOI: http://dx.doi.org/10.1016/j.compind.2018.12.001
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Zheng, Y., Predicting remaining useful
life based on Hilbert-Huang entropy with degradation model, Journal of
electrical and computer engineering, 2019, pp. 1- 11, 2019.
- DOI: http://dx.doi.org/10.1155/2019/3203959
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Compare, M., Bellani, L., Zio, E., Optimal
allocation of prognostics and health management capabilities to improve the
reliability of a power transmission network, Reliability engineering &
system safety, 184, pp. 164-180, 2019.
- DOI: http://dx.doi.org/10.1016/j.ress.2018.04.025
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Loukopoulos, P., Zolkiewski, G., Bennett,
I., Sampath, S., Pilidis, P., Duan, F., Sattar, T. and Mba, D., Reciprocating
compressor prognostics of an instantaneous failure mode utilising temperature
only measurements, Applied acoustics, 147, pp. 77-86, 2019.
- DOI: http://dx.doi.org/10.1016/j.apacoust.2017.12.003
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Lee, J., Kwon, D., Kim, N. and Lee, C.,
PHM-based wiring system damage estimation for near zero downtime in
manufacturing facilities, Reliability engineering & system safety, 184, pp.
213-218, 2019.
- DOI: http://dx.doi.org/10.1016/j.ress.2018.02.006
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Park, J., Kim, Y., Na, K. and Youn, B.
D., Variance of energy residual (VER): An efficient method for planetary gear
fault detection under variable-speed conditions, Journal of sound and
vibration, 453, pp. 253-267, 2019
- DOI: http://dx.doi.org/10.1016/j.jsv.2019.04.017
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Khan, S. and Yairi, T., A review on
the application of deep learning in system health management, Mechanical
systems and signal processing, 107, pp. 241-265, 2018.
- DOI: http://dx.doi.org/10.1016/j.ymssp.2017.11.024
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Shi, Z., Liu,
Z. and Lee, J., An auto-associative residual based approach for railway point
system fault detection and diagnosis, Measurement: Journal of the international
measurement confederation, 119, pp. 246-258, 2018.
- DOI: http://dx.doi.org/10.1016/j.measurement.2018.01.062
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