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2021 Vol.34, Issue 1 Preview Page

Research Paper

February 2021. pp. 25-33
Abstract
References
1
Caesarendra, W., Tjahjowidodo, T. (2017) A Review of Feature Extraction Methods in Vibration-Based Condition Monitoring and Its Application for Degradation Trend Estimation of Low- Speed Slew Bearing, Mach., 5(4), p.21. 10.3390/machines5040021
2
Chawla, N.V., Bowyer, K.W., Hall, L.O., Kegelmeyer, W.P. (2002) SMOTE: Synthetic Minority Over-Sampling Technique, J. Artif. Intell. Res., 16, pp.321~357. 10.1613/jair.953
3
Choi, J. (2020) PHM Practice-Case Study for Industrial Digitization: PHM Core Basic, Korea Society for Prognostics & Health Management, Seoul, South Korea, p.29.
4
Cortes, C., Jackel, L.D., Chiang, W.P. (1995) Limits on Learning Machine Accuracy Imposed by Data Quality, In Advances in Neural Information Processing Systems, pp.239~246.
5
Jahromi, A., Piercy, R., Cress, S., Service, J., Fan, W. (2009) An Approach to Power Transformer Asset Management using Health Index, IEEE Electr. Insul. Mag., 25(2), pp.20~34. 10.1109/MEI.2009.4802595
6
Jović, A., Brkić, K., Bogunović, N. (2015) A Review of Feature Selection Methods with Applications, 38th International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp.1200~1205. 10.1109/MIPRO.2015.7160458
7
Kang, Y.J., Hong, J., Lim, O.K., Noh, Y. (2017) Reliability analysis using parametric and nonparametric input modeling methods, J. Comput. Struct. Eng. Inst. Korea, 30(1), pp.87~94. 10.7734/COSEIK.2017.30.1.87
8
Kang, Y.J., Noh, Y., Lim, O.K. (2018) Kernel Density Estimation with Bounded Data, Struct. & Multidiscip. Optim., 57(1), pp.95~113. 10.1007/s00158-017-1873-3
9
Kim, Y.S., Lee, D.H., Kim, S.K. (2010) Fault Classification for Rotating Machinery using Support Vector Machines with Optimal Features Corresponding to Each Fault Type, Trans. Korean Soc. Mech. Eng. A, 34(11), pp.1681~1689. 10.3795/KSME-A.2010.34.11.1681
10
Ko, J.U., Jung, J.H., Kim, M., Kong, H.B., Youn, B.D. (2018) Noise Robust Fault Diagnosis Technique to Simultaneously Learn Classification and Denoising, In Proceedings of The Korean Soc. of Mech. Eng. (KSME), pp.165~167.
11
Lee, S.H., Ryu, S.M., Jeong, W.B. (2012) Vibration Analysis of Compressor Piping System with Fluid Pulsation, J. Mech. Sci. & Technol., 26(12), pp.3903~3909. 10.1007/s12206-012-0891-8
12
Lim, D.S., Yang, B.S., An, B.H., Tan, A., Kim, D.J. (2003) Condition Classification for Small Reciprocating Compressors Using Wavelet Transform and Artificial Neural Network, J. Korea Soc. Power Syst. Eng., 7(2), pp.29~35.
13
Sano, K., Mitsui, K. (1984) Analysis of Hermetic Rolling Piston Type Compressor Noise, and Countermeasures, Int. Compress. Eng. Conf., p.460.
14
Saxena, V., Chowdhury, N., Devendiran, S. (2013) Assessment of Gearbox Fault Detection using Vibration Signal Analysis and Acoustic Emission Technique, J. Mech. & Civil Eng., 7(4), pp.52~60. 10.9790/1684-0745260
15
Son, M.J., Jung, S.W., Hwang, E.J. (2019) A Deep Learning Based Over-Sampling Scheme for Imbalanced Data Classification, KIPS Trans. Softw. & Data Eng., 8(7), pp.311~316.
16
Son, Y., Ha, J., Lee, J. (2015) An Experimental Study on the Noise Source Identification of Rotary Compressor, Trans. Korea Soc. Noise & Vib. Eng., 25(11), pp.723~730. 10.5050/KSNVE.2015.25.11.723
17
Son, Y., Ha, J., Lee, J. (2017) The Noise Identification and Reduction of a Twin Rotary Compressor, Trans. Korea Soc. Noise & Vib. Eng., 27(3), pp.306~311. 10.5050/KSNVE.2017.27.3.306
18
Stockwell, D.R., Peterson, A.T. (2002) Effects of Sample Size on Accuracy of Species Distribution Models, Ecol. Model., 148(1), pp.1~13. 10.1016/S0304-3800(01)00388-X
19
Verstraete, D., Ferrada, A., Droguett, E.L., Meruane, V., Modarres, M. (2017) Deep Learning Enabled Fault Diagnosis using Time-Frequency Image Analysis of Rolling Element Bearings, Shock & Vib., 2017. 10.1155/2017/5067651
20
Wang, G., Kang, W., Wu, Q., Wang, Z., Gao, J. (2018) Generative Adversarial Network (GAN) based Data Augmentation for Palmprint Recognition, Digital Image Computing: Techniques and Applications (DICTA), pp.1~7. 10.1109/DICTA.2018.8615782
21
Yang, H.B., Zhang, J.A., Chen, L.L., Zhang, H.L., Liu, S.L. (2019) Fault Diagnosis of Reciprocating Compressor based on Convolutional Neural Networks with Multisource Raw Vibration Signals, Math. Probl. Eng., 2019. 10.1155/2019/6921975
Information
  • Publisher :Computational Structural Engineering Institute of Korea
  • Publisher(Ko) :한국전산구조공학회
  • Journal Title :Journal of the Computational Structural Engineering Institute of Korea
  • Journal Title(Ko) :한국전산구조공학회 논문집
  • Volume : 34
  • No :1
  • Pages :25-33
  • Received Date :2020. 11. 18
  • Revised Date :2020. 12. 10
  • Accepted Date : 2020. 12. 11