An adaptive method for health trend prediction of rotating bearings

Digital Signal Processing - Tập 35 - Trang 117-123 - 2014
Sheng Hong1, Zheng Zhou2, Enrico Zio3,4, Wenbin Wang5
1Science & Technology Laboratory on Reliability & Environmental Engineering, School of Reliability and System Engineering, Beihang University, Beijing, China
2Systems Engineering Research Institute, China State Shipbuilding Corporation (CSSC), China
3Department of Energy Polytechnic of Milan, Via Ponzio 34/3, 20133 Milan, Italy
4Ecole Central Paris et Supelec, Paris, France
5Dongling School of Economics and Management, University of Science and Technology Beijing, Beijing, China

Tài liệu tham khảo

Jardine, 2006, A review on machinery diagnostics and prognostics implementing condition-based maintenance, Mech. Syst. Signal Process., 20, 1483, 10.1016/j.ymssp.2005.09.012

Cheng, 2012, A rotating machinery fault diagnosis method based on local mean decomposition, Digit. Signal Process., 22, 356, 10.1016/j.dsp.2011.09.008

Messina, 2009, Nonstationary approaches to trend identification and denoising of measured power system oscillations, IEEE Trans. Power Syst., 24, 1798, 10.1109/TPWRS.2009.2030419

Pan, 2010, Bearing performance degradation assessment based on lifting wavelet packet decomposition and fuzzy c-means, Mech. Syst. Signal Process., 24, 559, 10.1016/j.ymssp.2009.07.012

Hu, 2012, Ensemble of data-driven prognostic algorithms for robust prediction of remaining useful life, Reliab. Eng. Syst. Saf., 103, 120, 10.1016/j.ress.2012.03.008

Bin, 2012, Early fault diagnosis of rotating machinery based on wavelet packets—empirical mode decomposition feature extraction and neural network, Mech. Syst. Signal Process., 27, 696, 10.1016/j.ymssp.2011.08.002

Di Maio, 2012, Combining Relevance Vector Machines and exponential regression for bearing residual life estimation, Mech. Syst. Signal Process., 31, 405, 10.1016/j.ymssp.2012.03.011

Peng, 2010, Current status of machine prognostics in condition-based maintenance: a review, Int. J. Adv. Manuf. Technol., 50, 297, 10.1007/s00170-009-2482-0

Heng, 2009, Rotating machinery prognostics: state of the art, challenges and opportunities, Mech. Syst. Signal Process., 23, 724, 10.1016/j.ymssp.2008.06.009

Si, 2011, Remaining useful life estimation—a review on the statistical data driven approaches, Eur. J. Oper. Res., 213, 1, 10.1016/j.ejor.2010.11.018

Benkedjouh, 2013, Remaining useful life estimation based on nonlinear feature reduction and support vector regression, Eng. Appl. Artif. Intell., 26, 1751, 10.1016/j.engappai.2013.02.006

Wei, 2013, A dynamic particle filter-support vector regression method for reliability prediction, Reliab. Eng. Syst. Saf., 119, 109, 10.1016/j.ress.2013.05.021

Widodo, 2011, Machine health prognostics using survival probability and support vector machine, Expert Syst. Appl., 38, 8430, 10.1016/j.eswa.2011.01.038

Liu, 2013, Multiple optimized online support vector regression for adaptive time series prediction, Measurement, 46, 2391, 10.1016/j.measurement.2013.04.033

Liao, 2013, A framework for predicting the remaining useful life of a single unit under time-varying operating conditions, IIE Trans., 45, 964, 10.1080/0740817X.2012.705451

Liao, 2014, Review of hybrid prognostics approaches for remaining useful life prediction of engineered systems, and an application to battery life prediction, IEEE Trans. Reliab., 63, 191, 10.1109/TR.2014.2299152

Sun, 2014, Prognostics uncertainty reduction by fusing on-line monitoring data based on a state-space-based degradation model, Mech. Syst. Signal Process., 45, 396, 10.1016/j.ymssp.2013.08.022

Soualhi, 2014, Prognosis of bearing failures using hidden Markov models and the adaptive neuro-fuzzy inference system, IEEE Trans. Ind. Electron., 61, 2864, 10.1109/TIE.2013.2274415

Song, 2013, Framework of designing an adaptive and multi-regime prognostics and health management for wind turbine reliability and efficiency improvement, Int. J. Adv. Comput. Sci. Appl., 4, 142

Lee, 2014, Prognostics and health management design for rotary machinery systems—reviews, methodology and applications, Mech. Syst. Signal Process., 42, 314, 10.1016/j.ymssp.2013.06.004

Baraldi, 2012, Investigation of uncertainty treatment capability of model-based and data-driven prognostic methods using simulated data, Reliab. Eng. Syst. Saf., 112, 94, 10.1016/j.ress.2012.12.004

Wu, 2010, An online adaptive condition-based maintenance method for mechanical systems, Mech. Syst. Signal Process., 24, 2985, 10.1016/j.ymssp.2010.04.003

E. Zio, Prognostics and health management of industrial equipment, in: Seifedine Kadry (Ed.), Diagnostics and Prognostics of Engineering Systems: Methods and Techniques, 2012, pp. 333–356.

Liao, 2010, Design of a reconfigurable prognostics platform for machine tools, Expert Syst. Appl., 37, 240, 10.1016/j.eswa.2009.05.004

Hong, 2012, Remaining useful life prognosis of bearing based on Gauss process regression, 1575

Xu, 2012, The research based on empirical mode decomposition in bearing fault diagnosis, Appl. Mech. Mater., 103, 225, 10.4028/www.scientific.net/AMM.103.225

Lei, 2013, A review on empirical mode decomposition in fault diagnosis of rotating machinery, Mech. Syst. Signal Process., 35, 108, 10.1016/j.ymssp.2012.09.015

Qiu, 2003, Robust performance degradation assessment methods for enhanced rolling element bearing prognostics, Adv. Eng. Inform., 17, 127, 10.1016/j.aei.2004.08.001

Hu, 2012, Dynamic degradation observer for bearing fault by MTS–SOM system, Mech. Syst. Signal Process., 36, 385, 10.1016/j.ymssp.2012.10.006

Yu, 2011, A hybrid feature selection scheme and self-organizing map model for machine health assessment, Appl. Soft Comput., 11, 4041, 10.1016/j.asoc.2011.03.026

Wang, 2001, Fault prognostics using dynamic wavelet neural networks, Artif. Intell. Eng. Des. Anal. Manuf., 15, 349, 10.1017/S0890060401154089

Lei, 2011, EEMD method and WNN for fault diagnosis of locomotive roller bearings, Expert Syst. Appl., 38, 7334, 10.1016/j.eswa.2010.12.095

Saha, 2010, Distributed prognostic health management with Gaussian process regression, 1

Liu, 2009, Intelligent analysis model of slope nonlinear displacement time series based on genetic-Gaussian process regression algorithm of combined kernel function, Chin. J. Rock Mech. Eng., 10, 2128

Rasmussen, 2006

Nectoux, 2012, PRONOSTIA: an experimental platform for bearings accelerated degradation tests, 1

Nguyen-Tuong, 2008, Local Gaussian process regression for real-time model-based robot control, 380

Shao, 2000, Prognosis of remaining bearing life using neural networks, Proc. Inst. Mech. Eng., Part I, J. Syst. Control Eng., 214, 217, 10.1243/0959651001540582

Thông tin
Thông tin xuất bản

Digital Signal Processing

Tập 35

117-123

Thông tin tác giả