Application of deterministic resampling particle filter to fatigue prognosis

The method based on a particle filter for a fatigue crack growth prognosis has proved to be a powerful and effective tool for developing prognostics and health management (PHM) technology. However, the widely used basic particle filter have the unavoidable particle impoverishment problem, which will make particles unable to approximate the true posterior probability density function of the system state and lead to a prognosis result with a large error. This paper proposes a fatigue crack growth prognosis method based on a deterministic resampling particle filter. The active structural health monitoring based on the Lamb wave is used for on-line crack length monitoring with piezoelectric transducers. With the on-line crack measurement, the crack state and crack growth model parameters are estimated for a fatigue crack growth prognosis. In addition, the deterministic resampling procedure is employed to overcome the particle impoverishment problem. The result shows the proposed crack growth prognosis method based on deterministic resampling particle filter can provide more satisfactory results than the basic particle filter.

[1]  Mehrdad Saif,et al.  An electrochemical model-based particle filter approach for Lithium-ion battery estimation , 2012, 2012 IEEE 51st IEEE Conference on Decision and Control (CDC).

[2]  Shenfang Yuan,et al.  Design of piezoelectric transducer layer with electromagnetic shielding and high connection reliability , 2012 .

[3]  Tiancheng Li,et al.  Deterministic resampling: Unbiased sampling to avoid sample impoverishment in particle filters , 2012, Signal Process..

[4]  Shenfang Yuan,et al.  On development of a multi-channel PZT array scanning system and its evaluating application on UAV wing box , 2009 .

[5]  George J. Vachtsevanos,et al.  A particle-filtering approach for on-line fault diagnosis and failure prognosis , 2009 .

[6]  Michael G. Pecht,et al.  Predicting long-term lumen maintenance life of LED light sources using a particle filter-based prognostic approach , 2015, Expert Syst. Appl..

[7]  Enrico Zio,et al.  Particle filtering prognostic estimation of the remaining useful life of nonlinear components , 2011, Reliab. Eng. Syst. Saf..

[8]  Fredrik Gustafsson,et al.  On Resampling Algorithms for Particle Filters , 2006, 2006 IEEE Nonlinear Statistical Signal Processing Workshop.

[9]  Victor Giurgiutiu,et al.  7 – PIEZOELECTRIC WAFER ACTIVE SENSORS , 2008 .

[10]  P. C. Paris,et al.  A Critical Analysis of Crack Propagation Laws , 1963 .

[11]  Nan Chen,et al.  Prognostics and Health Management: A Review on Data Driven Approaches , 2015 .

[12]  Jingjing He,et al.  Probabilistic inference of fatigue damage propagation with limited and partial information , 2015 .

[13]  Edward Sazonov,et al.  A novel damage index for damage identification using guided waves with application in laminated composites , 2014 .

[14]  Shen Yin,et al.  Intelligent Particle Filter and Its Application to Fault Detection of Nonlinear System , 2015, IEEE Transactions on Industrial Electronics.

[15]  N. Gordon,et al.  Novel approach to nonlinear/non-Gaussian Bayesian state estimation , 1993 .

[16]  Neil J. Gordon,et al.  A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking , 2002, IEEE Trans. Signal Process..

[17]  Z. Su,et al.  Identification of Damage Using Lamb Waves , 2009 .

[18]  Simon J. Godsill,et al.  On sequential Monte Carlo sampling methods for Bayesian filtering , 2000, Stat. Comput..

[19]  Juan M. Corchado,et al.  Fight sample degeneracy and impoverishment in particle filters: A review of intelligent approaches , 2013, Expert Syst. Appl..

[20]  A. Doucet,et al.  Particle Markov chain Monte Carlo methods , 2010 .

[21]  Jay Lee,et al.  Prognostics and health management design for rotary machinery systems—Reviews, methodology and applications , 2014 .

[22]  Juan Chiachio,et al.  Condition-based prediction of time-dependent reliability in composites , 2015, Reliab. Eng. Syst. Saf..

[23]  Christian Musso,et al.  Improving Regularised Particle Filters , 2001, Sequential Monte Carlo Methods in Practice.

[24]  Noureddine Zerhouni,et al.  Particle filter-based prognostics: Review, discussion and perspectives , 2016 .

[25]  M. Pitt,et al.  Filtering via Simulation: Auxiliary Particle Filters , 1999 .

[26]  Matteo Corbetta,et al.  Sequential Monte-Carlo sampling based on a committee of artificial neural networks for posterior state estimation and residual lifetime prediction , 2016 .

[27]  Enrico Zio,et al.  Model-based Monte Carlo state estimation for condition-based component replacement , 2009, Reliab. Eng. Syst. Saf..

[28]  George J. Vachtsevanos,et al.  A Particle Filtering Approach for On-Line Failure Prognosis in a Planetary Carrier Plate , 2007, Int. J. Fuzzy Log. Intell. Syst..

[29]  Rustam Stolkin,et al.  Particle Filter Tracking of Camouflaged Targets by Adaptive Fusion of Thermal and Visible Spectra Camera Data , 2014, IEEE Sensors Journal.

[30]  Hiroshi Tada,et al.  The stress analysis of cracks handbook , 2000 .

[31]  Jian Cai,et al.  Research on a Lamb Wave and Particle Filter-Based On-Line Crack Propagation Prognosis Method , 2016, Sensors.

[32]  Matteo Corbetta,et al.  On Dynamic State-Space models for fatigue-induced structural degradation , 2014 .

[33]  Aditi Chattopadhyay,et al.  A novel probabilistic approach for damage localization and prognosis including temperature compensation , 2016 .

[34]  S. D. Manning,et al.  Stochastic crack growth analysis methodologies for metallic structures , 1990 .