최소 단어 이상 선택하여야 합니다.
최대 10 단어까지만 선택 가능합니다.
다음과 같은 기능을 한번의 로그인으로 사용 할 수 있습니다.
NTIS 바로가기한국전산구조공학회논문집 = Journal of the computational structural engineering institute of Korea, v.34 no.1, 2021년, pp.25 - 33
김승일 (부산대학교 기계공학부 대학원) , 노유정 (부산대학교 기계공학부) , 강영진 (부산대학교 기계기술연구원) , 박선화 (LG전자 H&A연구소) , 안병하 (LG전자 H&A연구소)
With the development of machine learning techniques, various types of data such as vibration, temperature, and flow rate can be used to detect and diagnose abnormalities in machine conditions. In particular, in the field of the state monitoring of rotating machines, the fault diagnosis of machines u...
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.
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.
Choi, J. (2020) PHM Practice-Case Study for Industrial Digitization: PHM Core Basic, Korea Society for Prognostics & Health Management, Seoul, South Korea, p.29.
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.
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.
Jovic, A., Brkic, K., Bogunovic, 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.
Kang, Y.J., Noh, Y., Lim, O.K. (2018) Kernel Density Estimation with Bounded Data, Struct. & Multidiscip. Optim., 57(1), pp.95-113.
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.
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.
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.
Sano, K., Mitsui, K. (1984) Analysis of Hermetic Rolling Piston Type Compressor Noise, and Countermeasures, Int. Compress. Eng. Conf., p.460.
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.
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.
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.
Stockwell, D.R., Peterson, A.T. (2002) Effects of Sample Size on Accuracy of Species Distribution Models, Ecol. Model., 148(1), pp.1-13.
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.
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.
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.
※ AI-Helper는 부적절한 답변을 할 수 있습니다.