1 |
Yang, B. S. and K. J. Kim(2006), Application of Dempster-Shafer Theory in Fault Diagnosis of Induction Motors Using Vibration and Current Signal, Mechanical Systems and Signal Processing, Vol. 20, No. 2, pp. 403-420.
DOI
|
2 |
NI(2021), NI 9234, https://www.ni.com/pdf/manuals/374238a_02.pdf.
|
3 |
Shingala, M. C. and A. Rajyaguru(2015), Comparison of Post Hoc Tests for Unequal Variance, International Journal of New Technologies in Science and Engineering, Vol. 2, No. 5, pp. 22-33.
|
4 |
Wilcox, R. R.(2003), Applying Contemporary Statistical Techniques, pp. 299-301.
|
5 |
Yu, Y., D. Yu, and C. Junsheng(2006), A Roller Bearing Fault Diagnosis Method Based on EMD Energy Entropy and ANN, Journal of Sound and Vibration, Vol. 294, No. 1-2, pp. 269-277.
DOI
|
6 |
CMAK(2021), https://cmakusa.com/catalog/FLEXY-KITS.pdf.
|
7 |
Kowalski, C. T. and T. Orlowska-Kowalska(2003), Neural Networks Application for Induction Motor Faults Diagnosis, Mathematics and Computers in Simulation, Vol. 63, No. 3-5, pp. 435-448.
DOI
|
8 |
Duan, L., M. Yao, J. Wang, T. Bai, and L. Zhang(2016), Segmented Infraed Image Analysis for Rotating Machinery Fault Diagnosis, Infrared Physics and Technology, Vol. 77, pp. 267-276.
DOI
|
9 |
Jeon, B. C., J. H. Jung, B. D. Youn, Y. W. Kim, and Y. C. Bae(2015), Evaluation of Datum Unit for Diagnostics of Journal-Bearing Systems, Transactions of the Korean Society of Mechanical Engineers, Vol. 39, No. 8, pp. 801-806.
DOI
|
10 |
Ju, Y. J.(2020), Evaluation of Machine Learning Methods for Abnormality Detection and Diagnosis of Rotating Machine, Master Thesis, Inha University.
|
11 |
K Shipbuilding(2021), http://www.kshipbuilding.com/service/kor/yard_2020/yard.aspx.
|
12 |
Li, B., C. Mo-Yuen, Y. Tipsuwan, and J. C. Hung(2000), Neural-Network-Based Motor Rolling Bearing Fault Diagnosis, IEEE Transactions on Industrial Electronics, Vol. 47, No. 5, pp. 1060-1069.
DOI
|
13 |
Liu, R., B. Yang, E. Zio, and X. Chen(2018), Artificial Intelligence for fault Diagnosis of Rotating Machinery: A Review, Mechanical Systems and Signal Processing, Vol. 108, pp. 33-47.
DOI
|
14 |
Lei, Y., J. Lin, Z. He, and M. J. Zuo(2013), A Review on Empirical Mode Decomposition in Fault Diagnosis of Rotating Machinery, Mechanical Systems and Signal Processing. Vol. 35, No. 1-2, pp. 108-126.
DOI
|
15 |
Ocak, H. and K. A. Loparo(2001), A New Bearing Fault Detection and Diagnosis Scheme Based on Hidden Markov Modeling of Vibration Signals, IEEE International Conference Proceedings on Acoustics, Speech and Signal Processing, Vol. 5, pp. 3141-3144.
|
16 |
PCB(2021), PCB352C33, https://www.pcb.com/products?m=352C33.
|
17 |
Choi, J. H.(2013), Introduction of Failure Prediction and Prognostics and Health Management Technology, The Korean Society of Mechanical Engineers, Vol. 53, No. 7, pp. 24-34.
|
18 |
Hwang, H. S.(2020), Study on Fault Detection Algorithms Based on Time-Domain Statistical Analysis for Rolling Bearing, Master Thesis, Dong-A University.
|
19 |
Jung, U. and B. H. Koh(2015), Wavelet Energy-based Visualization and Classification of High-dimensional Signal for Bearing Fault Detection. Knowledge and Information Systems, Vol. 44, No. 1, pp. 197-215.
DOI
|