On‐line prognosis of fatigue crack propagation based on Gaussian weight‐mixture proposal particle filter

HIGHLIGHTSAn on‐line fatigue prognosis method is proposed based on an improved particle filter.A novel Gaussian weight‐mixture importance density is proposed in the particle filter.An on‐line dynamic updated state equation of fatigue crack propagation is proposed.The active guided wave based method is adopted for on‐line crack monitoring.Real fatigue tests of complex attachment lugs are performed for validation. ABSTRACT Accurate on‐line prognosis of fatigue crack propagation is of great meaning for prognostics and health management (PHM) technologies to ensure structural integrity, which is a challenging task because of uncertainties which arise from sources such as intrinsic material properties, loading, and environmental factors. The particle filter algorithm has been proved to be a powerful tool to deal with prognostic problems those are affected by uncertainties. However, most studies adopted the basic particle filter algorithm, which uses the transition probability density function as the importance density and may suffer from serious particle degeneracy problem. This paper proposes an on‐line fatigue crack propagation prognosis method based on a novel Gaussian weight‐mixture proposal particle filter and the active guided wave based on‐line crack monitoring. Based on the on‐line crack measurement, the mixture of the measurement probability density function and the transition probability density function is proposed to be the importance density. In addition, an on‐line dynamic update procedure is proposed to adjust the parameter of the state equation. The proposed method is verified on the fatigue test of attachment lugs which are a kind of important joint components in aircraft structures.

[1]  F. Chang,et al.  Detection and monitoring of hidden fatigue crack growth using a built-in piezoelectric sensor/actuator network: I. Diagnostics , 2004 .

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

[3]  Qiang Wang,et al.  Modeling nonlinearities of ultrasonic waves for fatigue damage characterization: theory, simulation, and experimental validation. , 2014, Ultrasonics.

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

[5]  Dawn An,et al.  Practical options for selecting data-driven or physics-based prognostics algorithms with reviews , 2015, Reliab. Eng. Syst. Saf..

[6]  Bin Liu,et al.  A reverberation‐ray matrix method for guided wave‐based non‐destructive evaluation , 2017, Ultrasonics.

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

[8]  Hyung Jin Lim,et al.  Nonlinear ultrasonic wave modulation for online fatigue crack detection , 2014 .

[9]  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 .

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

[11]  Standard Test Method for Measurement of Fatigue Crack Growth Rates 1 , 2016 .

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

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

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

[15]  Thomas B. Schön,et al.  Robust auxiliary particle filters using multiple importance sampling , 2014, 2014 IEEE Workshop on Statistical Signal Processing (SSP).

[16]  Yiwei Wang,et al.  Determination of Paris' law constants and crack length evolution via Extended and Unscented Kalman filter: An application to aircraft fuselage panels , 2016 .

[17]  Fu-Kuo Chang,et al.  Encyclopedia of structural health monitoring , 2009 .

[18]  Shenfang Yuan,et al.  Crack propagation monitoring in a full-scale aircraft fatigue test based on guided wave-Gaussian mixture model , 2016 .

[19]  Yoshio Arai,et al.  Use of ultrasonic back-reflection intensity for predicting the onset of crack growth due to low-cycle fatigue in stainless steel under block loading. , 2015, Ultrasonics.

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

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

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

[23]  Nando de Freitas,et al.  The Unscented Particle Filter , 2000, NIPS.

[24]  G. Zi,et al.  Probabilistic prognosis of fatigue crack growth for asphalt concretes , 2015 .

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

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

[27]  T. M. Hsu.  Analysis of Cracks at Attachment Lugs , 1981 .

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

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

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

[31]  Shenfang Yuan,et al.  A Multi-Response-Based Wireless Impact Monitoring Network for Aircraft Composite Structures , 2016, IEEE Transactions on Industrial Electronics.

[32]  Kai Goebel,et al.  Probabilistic fatigue damage prognosis of lap joint using Bayesian updating , 2015 .

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

[34]  Lalita Udpa,et al.  Particle filter based prognosis study for predicting remaining useful life of steam generator tubing , 2011, 2011 IEEE Conference on Prognostics and Health Management.

[35]  Wilson Wang,et al.  Enhanced fuzzy-filtered neural networks for material fatigue prognosis , 2013, Appl. Soft Comput..

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