Structural fatigue prognosis using limited sensor data

##plugins.themes.bootstrap3.article.main##

##plugins.themes.bootstrap3.article.sidebar##

Jingjing He Yongming Liu

Abstract

In this paper, a general framework for concurrent structural fatigue prognosis using limited sensor data is developed. The Empirical Mode Decomposition method is employed to reconstruct the structural dynamical response for the critical spot susceptible to fatigue damage. The sensor data available at limited locations measured from the usage monitor system are decoupled into several Intrinsic Mode Functions using the Empirical Mode Decomposition method. Those IMFs are applied to extrapolate the dynamic response for the critical spot where the direct response measurements are unavailable. The extrapolated dynamic response time series for the critical spot is then integrated with a physical fatigue crack growth model for fatigue damage prognosis. The proposed procedure is demonstrated using a multi degree-of-freedom (MDOF) cantilever beam example. The proposed method has great potential for the real-time decision making in the vehicle health management framework due to its ability for the concurrent damage prognosis.

How to Cite

He, J. ., & Liu, Y. . (2010). Structural fatigue prognosis using limited sensor data. Annual Conference of the PHM Society, 2(1). https://doi.org/10.36001/phmconf.2010.v2i1.1873
Abstract 138 | PDF Downloads 41

##plugins.themes.bootstrap3.article.details##

Keywords

PHM

References
(Adams and Nataraju, 2002) D. E. Adams and M. Nataraju. A Nonlinear Dynamical Systems Framework for Structural Diagnosis and Prognsis, International Journal of Engineering Science, vol. 40, pp. 1919-1941. 2002.

(Bao et al., 2009) C. Bao, H. Hao, Z. Li and X. Zhu, Time-varying System Identification Using a Newly Improved HHT Algorithm, Computers and Structures, vol. 87, pp. 1611-1623. 2009.

(Feldman, 1985) M. Feldman. Investigation of the Natural Vibrations of Machine Elements Using the Hilbert Transform. Soviet Machine Science, vol. 2, pp. 44-47. 1985.

(Feldman, 1997) M. Feldman. Nonlinear Free- Vibration Identification via the Hilbert Transform. Journal of Sound and Vibration, vol. 208, pp. 475 489. 1997.

(Gupta et al., 2007) S. Gupta, A. Ray and E. Keller. Online Fatigue Damage Monitoring by Ultrasonic
Measurements: A Symbolic Dynamics Approach, International Journal of Fatigue, vol. 29, pp. 1100- 1114. 2007.

(Gurley and Kareem, 1999) K. Gurley and A. Kareem. Application of Wavelet Transform in Earthquake, Wind, and Ocean Engineering, Journal of Engineering Structures, vol. 21, pp. 149-167. 1999.

(Haase and Widjajakusuma, 2003) M. Haase and J. Widjajakusuma. Damage Identication Based on Ridges and Maxima Lines of the Wavelet Transform, International Jounral Engineering Science, vol. 41, pp. 1423-1443. 2003.

(He et al., 2009) J. He and Y. Liu. A New Method for Concurrent Multi-scale Fatigue Damage Prognosis, The 7th International Workshop Structural Health Monitoring, Stanford University, CA, 2009.

(Huang et al., 1999) N. E. Huang, Z. Shen and S. R. Long, A New View of Nonlinear Water Waves: The Hilbert Spectrum, Annual Review of Fluid Mechanics, vol. 31, pp. 417-457. 1999.

(Huang et al., 1998) N. E. Huang, Z. Shen, S. R. Long, M. C. Wu, H. H. Shih, Q. Zheng, N.C. Yen, C.C. Tung and H.H. Liu The Empirical Mode Decomposition and Hilbert Spectrum for Nonlinear and Nonstationary Time Series Analysis. Proceedings of the Royal Society of London-Series A 454, pp. 903-995. 1998.

(Inman et al., 2005) D. J. Inman, C. R. Farrar, V. Lopes Jr and V. Steffen Jr. Damage Prognosis: For Aerospace, Civil and Mechanical System, John Wiley, pp.1-11, 2005.

(Janssen, 2004) M. Janssen. Fracture Mechanics, Taylor and Francis. 2004.

(Kyong et al., 2008) Y. Kyong, D. Kim, J. Dayou, K. Park, S. Wang, Mode Shape Reconstruction of an Impulse Excited Structure Using Continuous Scanning Laser Doppler Vibrometer and Empirical Mode Decomposition, Review of Scientific Instruments, vol. 79.

(Li et al., 2001) Z. X. Li, T. H. T. Chan and J. M. Ko. Fatigue Analysis and Life Prediction of Bridges with Structural Health Monitoring Data - Part I: Methodology and Strategy, International Journal of Fatigue, vol. 23, pp. 45-53. 2001.

(Link and Weiland, 2009) M. Link and M. Weiland. Damage Identification by Multi-model Updating in the Modal and in the Time Domain, Mechanical System and Signal Processing, vol. 23, pp.1734- 1746. 2009.

(Lu and Liu, 2010) Z. Lu and Y. Liu. Small Time Scale Fatigue Crack Growth Analysis, International Journal of Fatigue. vol. 32, pp. 1306-1321.

(Luk and Damper, 2006) R. W. P. Luk and R. I. Damper. Non-parametric Linear Time-invariant System Identification by Discrete Wavelet Transforms, Digital Signal Processing, vol. 16, pp. 303-319. 2006.

(Papazian et al., 2007) J. M. Papazian, J. Nardiello, R. P. Silberstein, G. Welsh, D. Grundy, C. Craven, L. Evans, N. Goldfine, J. E. Michaels, T. E. Michaels, Y. Li and C. Laird. Sensor for Monitoring Early Stage Fatigue Cracking, International Journal of Fatigue, vol. 29, pp. 1668-1680. 2007

(Papazian et al., 2009) J. M. Papazian, E. L. Anagnostou, S. J. Engel, D. Hoitsma, J. Madsen, R. P. Silberstein, G. Welsh and J. B. Whiteside. A Structural Integrity Prognosis System, Engineering Fracture Mechanics, vol. 79, pp. 620-632. 2009

(Paris and Erdogan, 1963) P. C. Paris, F. Erdogan. A Critical Analysis of Crack Propagation Laws. Journal of Basic Engineering, vol. 85, pp. 528-534. 1963.

(Patankar et al., 1998) R. Patankar, A. Ray and A. Lakhtakia. A State-space Model of Fatigue Crack Growth, International Journal of Fracture, vol. 90, pp. 235-249. 1998.

(Poon and Chang, 2007) C. W. Poon and C. C. Chang, Identification of Nonlinear Elastic Structures Using Empirical Mode Decomposition and Nonlinear Normal Modes, Smart structures and Systems, vol. 3, pp. 1-15. 2007.

(Porter, 1972) T. R. Porter. Method of Analysis and Prediction for Variable Amplitude Fatigue Crack Growth, Engineering Fracture Mechanics, vol. 4, pp. 717-736. 1972.

(Pislaru et al., 2003) C. Pislaru, J. M. Freeman and D. G. Ford. Modal Parameter Identification for CNC Machine Tools Using Wavelet Transform, International Journal of Machine Tools & Manufacture, vol. 43, pp. 987-993. 2003.

(Riera et al., 2004) J. J. Riera, J. Watanabe, I. Kazuki, M. Naoki, E. Aubert, T. Ozaki and R. Kawashima. A State-space Model of the Hemodynamic Approach: Nonlinear Filtering of BOLD Signals, NeuroImage, Vol. 21, pp. 547-567. 2004.

(Tan et al., 2008) J. Tan, Y. Liu, L. Wang and W. Yang. Identification of Modal Parameters of a System with High Damping and Closely Spaced Modes by Combining Continuous Wavelet Transform with Pattern Search, Mechanical System and Signal Processing, vol. 22, pp. 1055-1060. 2008.

(Yan and Gao, 2006) R. Yan and R. X. Gao. Hilbert- Huang Transform-Based Vibration Signal Analysis for Machine Health Monitoring, IEEE Transactions on Instrumentation and Measurement, vol. 55, pp. 2320-2329. 2006.

(Yang et al., 2003) J. N. Yang, Y. Lei, S. Pan and N. E. Huang, System Identification of Linear Structures Based on Hilbert-Huang Spectral Analysis. Part 1: Normal Modes, Earthquake engineering and structural dynamics, vol. 32, pp. 1443-1467. 2003.
Section
Technical Papers

Most read articles by the same author(s)

<< < 1 2