Research on a Lamb Wave and Particle Filter-Based On-Line Crack Propagation Prognosis Method

Prognostics and health management techniques have drawn widespread attention due to their ability to facilitate maintenance activities based on need. On-line prognosis of fatigue crack propagation can offer information for optimizing operation and maintenance strategies in real-time. This paper proposes a Lamb wave-particle filter (LW-PF)-based method for on-line prognosis of fatigue crack propagation which takes advantages of the possibility of on-line monitoring to evaluate the actual crack length and uses a particle filter to deal with the crack evolution and monitoring uncertainties. The piezoelectric transducers (PZTs)-based active Lamb wave method is adopted for on-line crack monitoring. The state space model relating to crack propagation is established by the data-driven and finite element methods. Fatigue experiments performed on hole-edge crack specimens have validated the advantages of the proposed method.

[1]  Michael G. Pecht,et al.  Sensor Systems for Prognostics and Health Management , 2010, Sensors.

[2]  Shenfang Yuan,et al.  A quantitative multidamage monitoring method for large-scale complex composite , 2013 .

[3]  Victor Giurgiutiu,et al.  Piezoelectric Wafer Embedded Active Sensors for Aging Aircraft Structural Health Monitoring , 2002 .

[4]  Bin Zhang,et al.  Machine Condition Prediction Based on Adaptive Neuro–Fuzzy and High-Order Particle Filtering , 2011, IEEE Transactions on Industrial Electronics.

[5]  Christian Willberg,et al.  Experimental and Theoretical Analysis of Lamb Wave Generation by Piezoceramic Actuators for Structural Health Monitoring , 2012 .

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

[7]  F. Yuan,et al.  Diagnostic Lamb waves in an integrated piezoelectric sensor/actuator plate: analytical and experimental studies , 2001 .

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

[9]  Ghassan M. Atmeh,et al.  Conceptual Implementation of an Automated Structural Health Monitoring System , 2013 .

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

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

[12]  Ming Jian Zuo,et al.  An integrated framework for online diagnostic and prognostic health monitoring using a multistate deterioration process , 2014, Reliab. Eng. Syst. Saf..

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

[14]  T. Başar,et al.  A New Approach to Linear Filtering and Prediction Problems , 2001 .

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

[16]  Michael Pecht,et al.  Prognostics uncertainty reduction by fusing on-line monitoring data based on a state-space-based degradation model , 2014 .

[17]  K. Goebel,et al.  Bayesian model selection and parameter estimation for fatigue damage progression models in composites , 2015 .

[18]  Jae Hong Kim,et al.  Lamb Wave Line Sensing for Crack Detection in a Welded Stiffener , 2014, Sensors.

[19]  Masoud Rabiei,et al.  A recursive Bayesian framework for structural health management using online monitoring and periodic inspections , 2013, Reliab. Eng. Syst. Saf..

[20]  Joel P. Conte,et al.  A recursive Bayesian approach for fatigue damage prognosis: An experimental validation at the reliability component level , 2014 .

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

[22]  Enrico Zio,et al.  Predictive Maintenance by Risk Sensitive Particle Filtering , 2014, IEEE Transactions on Reliability.

[24]  Fu-Kuo Chang,et al.  A structural health monitoring fastener for tracking fatigue crack growth in bolted metallic joints , 2012 .

[25]  S. Winterstein,et al.  Random Fatigue: From Data to Theory , 1992 .

[26]  G. Bezine,et al.  Advantages of the J-integral approach for calculating stress intensity factors when using the commercial finite element software ABAQUS , 2005 .

[27]  Tatsuo Sakai,et al.  Statistical analysis of fatigue crack growth behavior for grade B cast steel , 2011 .

[28]  J.W. Sheppard,et al.  IEEE Standards for Prognostics and Health Management , 2008, IEEE Aerospace and Electronic Systems Magazine.

[29]  Ian K. Jennions,et al.  A review of Integrated Vehicle Health Management tools for legacy platforms: Challenges and opportunities , 2013 .

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

[31]  Wieslaw J. Staszewski,et al.  Health Monitoring Of Aerospace Structures: Smart Sensor Technologies And Signal Processing , 2017 .

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

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