Damage evaluation by a guided wave-hidden Markov model based method

Guided wave based structural health monitoring has shown great potential in aerospace applications. However, one of the key challenges of practical engineering applications is the accurate interpretation of the guided wave signals under time-varying environmental and operational conditions. This paper presents a guided wave-hidden Markov model based method to improve the damage evaluation reliability of real aircraft structures under time-varying conditions. In the proposed approach, an HMM based unweighted moving average trend estimation method, which can capture the trend of damage propagation from the posterior probability obtained by HMM modeling is used to achieve a probabilistic evaluation of the structural damage. To validate the developed method, experiments are performed on a hole-edge crack specimen under fatigue loading condition and a real aircraft wing spar under changing structural boundary conditions. Experimental results show the advantage of the proposed method.

[1]  Paul D. Gader,et al.  Generalized hidden Markov models. II. Application to handwritten word recognition , 2000, IEEE Trans. Fuzzy Syst..

[2]  Wang Qiang,et al.  Baseline-free Imaging Method based on New PZT Sensor Arrangements , 2009 .

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

[4]  Wieslaw J. Staszewski,et al.  Lamb wave based structural damage detection using cointegration and fractal signal processing , 2014 .

[5]  Selin Aviyente,et al.  Prognosis of Gear Failures in DC Starter Motors Using Hidden Markov Models , 2011, IEEE Transactions on Industrial Electronics.

[6]  Li Cheng,et al.  On Selection of Data Fusion Schemes for Structural Damage Evaluation , 2009 .

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

[8]  Lin Ye,et al.  Guided Lamb waves for identification of damage in composite structures: A review , 2006 .

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

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

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

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

[13]  Aaron F. Bobick,et al.  Parametric Hidden Markov Models for Gesture Recognition , 1999, IEEE Trans. Pattern Anal. Mach. Intell..

[14]  Carey Bunks,et al.  CONDITION-BASED MAINTENANCE OF MACHINES USING HIDDEN MARKOV MODELS , 2000 .

[15]  Xiang Li,et al.  A Physically Segmented Hidden Markov Model Approach for Continuous Tool Condition Monitoring: Diagnostics and Prognostics , 2012, IEEE Transactions on Industrial Informatics.

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

[17]  Joseph L. Rose,et al.  Dispersion Curves in Guided Wave Testing , 2003 .

[18]  Antonia Papandreou-Suppappola,et al.  On the Use of Hidden Markov Modeling and Time-frequency Features for Damage Classification in Composite Structures , 2009 .

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

[20]  Lawrence R. Rabiner,et al.  A tutorial on hidden Markov models and selected applications in speech recognition , 1989, Proc. IEEE.

[21]  Roshan Rammohan,et al.  Exploratory Investigations for Intelligent Damage Prognosis using Hidden Markov Models , 2005, 2005 IEEE International Conference on Systems, Man and Cybernetics.

[22]  Hubert Razik,et al.  Prognosis of Bearing Failures Using Hidden Markov Models and the Adaptive Neuro-Fuzzy Inference System , 2014, IEEE Transactions on Industrial Electronics.

[23]  Hoon Sohn,et al.  Effects of environmental and operational variability on structural health monitoring , 2007, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[24]  L. Rabiner,et al.  An introduction to hidden Markov models , 1986, IEEE ASSP Magazine.

[25]  José Rodellar,et al.  Data-driven methodology to detect and classify structural changes under temperature variations , 2014 .

[26]  Hoon Sohn,et al.  Instantaneous reference-free crack detection based on polarization characteristics of piezoelectric materials , 2007 .

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

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

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

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

[32]  Hoon Sohn,et al.  Combination of a Time Reversal Process and a Consecutiv Outlier Analysis for Baseline-free Damage Diagnosis , 2006 .

[33]  Spilios D. Fassois,et al.  A global statistical model based approach for vibration response-only damage detection under various temperatures: A proof-of-concept study , 2014 .

[34]  V. Giurgiutiu.  Tuned Lamb Wave Excitation and Detection with Piezoelectric Wafer Active Sensors for Structural Health Monitoring , 2005 .

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

[36]  P. Baruah,et al.  HMMs for diagnostics and prognostics in machining processes , 2005 .

[37]  Daniel William Huff,et al.  Adaptive Methods within a Sequential Bayesian Approach for Structural Health Monitoring , 2013 .

[38]  Liu Meie,et al.  液晶エラストマー片持梁の光‐熱‐機械的駆動の曲げとスナップ動力学 , 2014 .