Browse > Article
http://dx.doi.org/10.7837/kosomes.2022.28.2.346

Positioning-error Analysis of Vibration Sensors for Prognostics and Health Management in Rotating System  

Jang, Jaewon (Graduate School of Mokpo National Maritime University)
Han, Zhiqiang (Graduate School of Mokpo National Maritime University)
Zhang, Haiyang (Graduate School of Mokpo National Maritime University)
Oh, Daekyun (Department of Naval Architecture and Ocean Engineering, Mokpo National Maritime University)
Publication Information
Journal of the Korean Society of Marine Environment & Safety / v.28, no.2, 2022 , pp. 346-353 More about this Journal
Abstract
Recently, studies on the integrity of rotating machines, such as gantry cranes, which are used in the shipbuilding industry, have been actively conducted. Gantry cranes are driven at relatively low revolutions per minute (RPM), are frequently operated and stopped, and are impacted by external environmental factors, such as shock and noise in the measurement data. The purpose of this study was to construct a replica of a gantry crane hoist used in indoor shipbuilding and analyze the acquired data for errors caused by the shift in operating conditions (RPM) and the change in the position of the data acquisition sensor. Consequently, we observed that the error caused by differences in sensor positions did not occur significantly under low operating conditions but occurred significantly under relatively high operating conditions. Thus, we determined that both the operating condition and position of the acquisition sensor affected the data acquired by the rotary machine.
Keywords
Rotary machine; PHM (Prognostics and Health Management); Vibration sensor; ANOVA (Analysis of Variance); Statistical analysis;
Citations & Related Records
연도 인용수 순위
  • Reference
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