À̸§ KSPHM À̸ÞÀÏ phm@phm.or.kr
ÀÛ¼ºÀÏ 2019-12-03 Á¶È¸¼ö 3762
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[PHM News Letter vol.6] PHM °ü·Ã±¹³»¿Ü ÃֽŠ´ëÇ¥ ³í¹®

 

 

 

 

 

u  Zhang, Y., Xiong, R., He, H., Qu, X. and Pecht, M., Aging characteristics-based health diagnosis and remaining useful life prognostics for lithium-ion batteries, ETransportation,1, pp. 100004_1-10, 2019.

DOI: https://doi.org/10.1016/j.etran.2019.100004

 

u  Zhang, L., Lin, J., Liu, B., Zhang, Z., Yan, X. and Wei, M., A review on deep learning applications in prognostics and health management, IEEE access, 7, pp. 162415-162438, 2019.

DOI: https://doi.org/10.1109/access.2019.2950985

 

u  Hou, G.Q. and Lee, C.M., Estimation of the defect width on the outer race of a rolling element bearing under time-varying speed conditions, Shock and vibration, 2019, pp. 8479395_1-11, 2019.

DOI: https://doi.org/10.1155/2019/8479395

 

u  Ham, S., Han, S.Y., Kim, S., Park, H.J., Park, K.J. and Choi, J.H., A comparative study of fault diagnosis for train door system: Traditional versus deep learning approaches, Sensors, 19(23), pp. 5160_1-15, 2019.

DOI: https://doi.org/10.3390/s19235160

 

u  Venugopal, P., State-of-health estimation of li-ion batteries in electric vehicle using IndRNN under variable load condition, Energies, 12(22), pp. 4338_1-29, 2019.

DOI: https://doi.org/10.3390/en12224338

 

u  Daher, A., Hoblos, G., Khalil, M. and Chetouani, Y., New prognosis approach for preventive and predictive maintenance—Application to a distillation column, Chemical engineering research and design, 153, pp.162-174, 2020.

DOI: https://doi.org/10.1016/j.cherd.2019.10.029

 

u  Li, X., Yang, X., Yang, Y., Bennett, I. and Mba, D., An intelligent diagnostic and prognostic framework for large-scale rotating machinery in the presence of scarce failure data, Structural health monitoring, published online, pp. 1475921719884019, 2019.

DOI: https://doi.org/10.1177/1475921719884019

 

u  Aivaliotis, P., Georgoulias, K. and Chryssolouris, G., The use of digital twin for predictive maintenance in manufacturing, International journal of computer integrated manufacturing, published online, pp.1067-1080, 2019.

DOI: https://doi.org/10.1080/0951192X.2019.1686173

 

u  Goyal, D., Choudhary, A., Pabla, B.S. and Dhami, S.S., Support vector machines based non-contact fault diagnosis system for bearings, Journal of intelligent manufacturing, published online, pp.1-15, 2019.

DOI: https://doi.org/10.1007/s10845-019-01511-x

 

u  She, C., Wang, Z., Sun, F. and Zhang, L., Battery aging assessment for real-world electric buses based on incremental capacity analysis and radial basis function neural network, IEEE transactions on industrial informatics, early access article, 2019.

DOI: https://doi.org/10.1109/TII.2019.2951843

 

 


 

 

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