Crack propagation monitoring in a full-scale aircraft fatigue test based on guided wave-Gaussian mixture model

For aerospace application of structural health monitoring (SHM) technology, the problem of reliable damage monitoring under time-varying conditions must be addressed and the SHM technology has to be fully validated on real aircraft structures under realistic load conditions on ground before it can reach the status of flight test. In this paper, the guided wave (GW) based SHM method is applied to a full-scale aircraft fatigue test which is one of the most similar test status to the flight test. To deal with the time-varying problem, a GW-Gaussian mixture model (GW-GMM) is proposed. The probability characteristic of GW features, which is introduced by time-varying conditions is modeled by GW-GMM. The weak cumulative variation trend of the crack propagation, which is mixed in time-varying influence can be tracked by the GW-GMM migration during on-line damage monitoring process. A best match based Kullback–Leibler divergence is proposed to measure the GW-GMM migration degree to reveal the crack propagation. The method is validated in the full-scale aircraft fatigue test. The validation results indicate that the reliable crack propagation monitoring of the left landing gear spar and the right wing panel under realistic load conditions are achieved.

[1]  P. S. Heyns,et al.  Combining synchronous averaging with a Gaussian mixture model novelty detection scheme for vibration-based condition monitoring of a gearbox , 2012 .

[2]  Guang Meng,et al.  A correlation filtering-based matching pursuit (CF-MP) for damage identification using Lamb waves , 2006 .

[3]  Jay Lee,et al.  Wind turbine performance assessment using multi-regime modeling approach , 2012 .

[4]  Volker Tresp,et al.  Improved Gaussian Mixture Density Estimates Using Bayesian Penalty Terms and Network Averaging , 1995, NIPS.

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

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

[7]  Lingyu Yu,et al.  Shear lag solution for tuning ultrasonic piezoelectric wafer active sensors with applications to Lamb wave array imaging , 2010 .

[8]  Jeong-Beom Ihn,et al.  Pitch-catch Active Sensing Methods in Structural Health Monitoring for Aircraft Structures , 2008 .

[9]  Xinlin Qing,et al.  Prediction of Progressive Damage State at the Hot Spots using Statistical Estimation , 2010 .

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

[11]  Fang Fang,et al.  Dynamic evaluation of wind turbine health condition based on Gaussian mixture model and evidential reasoning , 2013 .

[12]  Paul D. Wilcox,et al.  The temperature stability of guided wave structural health monitoring systems , 2006 .

[13]  Antonia Papandreou-Suppappola,et al.  An adaptive learning damage estimation method for structural health monitoring , 2015 .

[14]  A. Raftery,et al.  Model-based Gaussian and non-Gaussian clustering , 1993 .

[15]  Wieslaw Ostachowicz,et al.  Circular sensing networks for guided waves based structural health monitoring , 2016 .

[16]  Christian Brauner,et al.  Non-damage-related influences on Lamb wave–based structural health monitoring of carbon fiber–reinforced plastic structures , 2014 .

[17]  M. Aliabadi,et al.  Assessment of delay-and-sum algorithms for damage detection in aluminium and composite plates , 2014 .

[18]  Hyung Jin Lim,et al.  Reference-free fatigue crack detection using nonlinear ultrasonic modulation under various temperature and loading conditions , 2014 .

[19]  Charles R. Farrar,et al.  Machine learning algorithms for damage detection under operational and environmental variability , 2011 .

[20]  Daniel J. Inman,et al.  Detection and localization of fatigue crack with nonlinear instantaneous baseline , 2016 .

[21]  Tadeusz Uhl,et al.  Remote Monitoring of Fatigue Cracks Growth in the Aircraft Structure Based on Active Piezosensor Network during the Full Scale Fatigue Test , 2013 .

[22]  Shiri Gordon,et al.  An efficient image similarity measure based on approximations of KL-divergence between two gaussian mixtures , 2003, Proceedings Ninth IEEE International Conference on Computer Vision.

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

[24]  Hoon Sohn,et al.  Locating fatigue damage using temporal signal features of nonlinear Lamb waves , 2015 .

[25]  Jennifer E. Michaels,et al.  Multipath ultrasonic guided wave imaging in complex structures , 2015 .

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

[27]  Zhongqing Su,et al.  Evaluation of fatigue cracks using nonlinearities of acousto-ultrasonic waves acquired by an active sensor network , 2012 .

[28]  Matthias Buderath,et al.  The need for guidance on integrating structural health monitoring within military aircraft systems , 2014 .

[29]  Chunhui Yang,et al.  Assessment of delamination in composite beams using shear horizontal (SH) wave mode , 2007 .

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

[31]  Qiang Wang,et al.  Acousto-ultrasonics-based fatigue damage characterization: Linear versus nonlinear signal features , 2014 .

[32]  Nesrin Sarigul-Klijn,et al.  A review of uncertainty in flight vehicle structural damage monitoring, diagnosis and control: Challenges and opportunities , 2010 .

[33]  Shenfang Yuan,et al.  On-line updating Gaussian mixture model for aircraft wing spar damage evaluation under time-varying boundary condition , 2014 .

[34]  Eric B. Flynn,et al.  Multi-wave-mode, multi-frequency detectors for guided wave interrogation of plate structures , 2014 .

[35]  Jochen Moll,et al.  Efficient temperature compensation strategies for guided wave structural health monitoring. , 2010, Ultrasonics.

[36]  Yishou Wang,et al.  Validation and evaluation of damage identification using probability-based diagnostic imaging on a stiffened composite panel , 2015 .

[37]  M. Wolff,et al.  Statistical Classifiers for Structural Health Monitoring , 2009, IEEE Sensors Journal.

[38]  Li Cheng,et al.  Artificial Neural Network (ANN)-based Crack Identification in Aluminum Plates with Lamb Wave Signals: , 2009 .

[39]  Keith Worden,et al.  A multiresolution approach to cointegration for enhanced SHM of structures under varying conditions – An exploratory study , 2014 .

[40]  Kuldeep Lonkar,et al.  A novel physics-based temperature compensation model for structural health monitoring using ultrasonic guided waves , 2014 .

[41]  W. Staszewski,et al.  Health monitoring of aerospace composite structures – Active and passive approach , 2009 .

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

[43]  J. Michaels,et al.  Feature Extraction and Sensor Fusion for Ultrasonic Structural Health Monitoring Under Changing Environmental Conditions , 2009, IEEE Sensors Journal.

[44]  Mamdouh M. Salama,et al.  Development of a Real-Time Active Pipeline Integrity Detection System , 2009 .

[45]  Neal N. McCollom,et al.  PHM on the F-35 fighter , 2011, 2011 IEEE Conference on Prognostics and Health Management.

[46]  D. Rubin,et al.  Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper , 1977 .

[47]  Patrice Masson,et al.  Correlation-based imaging technique for fatigue monitoring of riveted lap-joint structure , 2014 .

[48]  F. Yuan,et al.  Guided wave generation, sensing and damage detection using in-plane shear piezoelectric wafers , 2014 .