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ÀÛ¼ºÀÏ 2019-03-06 Á¶È¸¼ö 2286
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PHM °ü·Ã Çмú Àú³Î ¹× ³í¹® (Vol.3)

 


Ÿ Li, H., Sun, J., Ma, H., Tian, Z., and Li, Y., A novel method based upon modified composite spectrum and relative entropy for degradation feature extraction of hydraulic pump, Mechanical systems and signal processing, 114, pp. 399-412, 2019.

DOI: http://dx.doi.org/10.1016/j.ymssp.2018.04.040

 

Ÿ Hassani, N., Seyed, M. M., Jin, X., and Ni, J., Physics-based Gaussian process for the health monitoring for a rolling bearing, Acta astronautica, 154, pp. 133-139, 2019.

DOI: http://dx.doi.org/10.1016/j.actaastro.2018.10.029

 

Ÿ Listou Ellefsen, A., Bj©ªrlykhaug, E., ¨¡s©ªy, V., Ushakov, S., and Zhang, H., Remaining useful life predictions for turbofan engine degradation using semi-supervised deep architecture, Reliability engineering & system safety, 183, pp. 240-251, 2019.

DOI: http://dx.doi.org/10.1016/j.ress.2018.11.027

 

Ÿ Sierra, G., Orchard, M., Goebel, K., and Kulkarni, C., Battery health management for small-size rotary-wing electric unmanned aerial vehicles: An efficient approach for constrained computing platforms, Reliability engineering & system safety, 182, pp. 166-178, 2019.

DOI: http://dx.doi.org/10.1016/j.ress.2018.04.030

 

Ÿ Li, X., Zhang, W., and Ding, Q., Deep learning-based remaining useful life estimation of bearings using multi-scale feature extraction, Reliability engineering & system safety, 182, pp. 208-218, 2019.

DOI: http://dx.doi.org/10.1016/j.ress.2018.11.011

 

Ÿ Jeong, H., Park, B., Park, S., Min, H., and Lee, S., Fault detection and identification method using observer-based residuals, Reliability engineering & system safety, 184, pp. 2740, 2019.

DOI: http://dx.doi.org/10.1016/j.ress.2018.02.007

 

Ÿ Climente-Alarcon, V., Arkkio, A., and Antonino-Daviu, J., Study of thermal stresses developed during a fatigue test on an electrical motor rotor cage, International journal of fatigue, 120, pp. 56-64, 2019.

DOI: http://dx.doi.org/10.1016/j.ijfatigue.2018.11.003

 

Ÿ Loukopoulos, P., Zolkiewski, G., Bennett, I., Sampath, S., Pilidis, P., Li, X., and Mba, D., Abrupt fault remaining useful life estimation using measurements from a reciprocating compressor valve failure, Mechanical systems and signal processing, 121, pp. 359-372, 2019.

DOI: http://dx.doi.org/10.1016/j.ymssp.2018.09.033

 

Ÿ Han, C., and Lee, H., A field-applicable health monitoring method for photovoltaic system, Reliability engineering & system safety, 184, pp. 219-227, 2019.

DOI: http://dx.doi.org/10.1016/j.ress.2018.01.002

 

Ÿ Chen, J., Jing, H., Chang, Y., and Liu, Q., Gated recurrent unit based recurrent neural network for remaining useful life prediction of nonlinear deterioration process, Reliability engineering & system safety, 185, pp. 372-382, 2019.

DOI: http://dx.doi.org/10.1016/j.ress.2019.01.006

 

 


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