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A multidisciplinary approach to structural health monitoring and damage prognosis of aerospace hotspots

Published online by Cambridge University Press:  03 February 2016

A. Chattopadhyay ,
P. Peralta ,
A. Papandreou-Suppappola  and
N. Kovvali
A. Chattopadhyay
Affiliation:
Department of Mechanical and Aerospace Engineering, Arizona State University Tempe, Arizona USA
A. Papandreou-Suppappola
Affiliation:
[email protected], Department of Electrical Engineering, Arizona State University Tempe, Arizona, USA
N. Kovvali
Affiliation:
[email protected]
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Abstract

The health monitoring and damage prognosis of aerospace hotspots is important for reducing maintenance costs and increasing in-service capacity of aging aircraft. One of the leading causes of structural failure in aerospace vehicles is fatigue damage. Based on the physical mechanism of damage nucleation and growth, a physics-based multiscale model is considered for fatigue damage assessment in metallic aircraft structures. A guided-wave based sensing approach is utilised to enable effective damage detection in a common structural hotspot: a lug joint. Finite element analysis is carried out with piezoelectric wafers bonded to the host structure and the simulated sensor signals are analysed. A damage classification strategy is developed, which integrates physically motivated time-frequency approaches with advanced stochastic modelling techniques. In particular, a variational Bayesian learning scheme is used to estimate the optimal model complexity automatically from the data, adapting the classifier for real-time use. Classification performance is studied as a function of signal-to-noise ratio and results are reported for the detection of fatigue crack damage in the lug joint. An adaptive hybrid prognosis model is proposed, which estimates the residual useful life of structural hotspots using damage condition information obtained in real-time.

Type
Research Article
Information
The Aeronautical Journal , Volume 113 , Issue 1150: Flight structures fundamental research in the United States of America: A Special Issue – PART II , December 2009 , pp. 799 - 810
Copyright
Copyright © Royal Aeronautical Society 2009 

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References

1. Farrar, C.R. and Worden, K., An introduction to structural health monitoring, 2007, Phil Trans R Soc A, 365, pp 303–315.Google Scholar
2. Farrar, C.R. and Lieven, N.A.J., Damage prognosis: the future of structural health monitoring, Phil Trans R Soc A, 2007, 365, pp 623–632.Google Scholar
3. Staszewski, W.J., Boller, C. and Tomlinson, G.R., Health Monitoring of Aerospace Structures, 2003, Wiley, UK.Google Scholar
4. Production processes, United States Air Force Scientific Advisory Board, 1992, Report of the AdHoc Committee on Air Force Aircraft Jet Engine Manufacturing and SAF/AQQS: Pentagon, Washington, DC, USA.Google Scholar
5. Lemaitre, J. and Desmorat, R., Engineering Damage Mechanics, 2005, Springer, Berlin, Heidelberg, New York.Google Scholar
6. Soni, S., Das, S. and Chattopadhyay, A., Simulation of damage-features in a lug joint using guided waves, J Intelligent Material Systems and Structures, August 2009, 20, pp 1451–1464.Google Scholar
7. Soni, S., Banerjee, S., Das, S. and Chattopadhyay, A., Simulation of damage-features in complex joint using guided waves, 2008, Proc SPIE Smart Structures and Materials and Nondestructive Evaluation and Health Monitoring, San Diego, California, invited Session on Information Management for Structural Health Monitoring.Google Scholar
8. Soni, S., Das, S. and Chattopadhyay, A., Guided wave based technique for optimal sensor placement and damage detection, 2009, Proc 50th AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics and Materials Conference, Palm Springs, California.Google Scholar
9. Soni, S., Das, S. and Chattopadhyay, A., Sensor sensitivity studies to monitor crack growth in lug joint structures, 2009, Proc SPIE Smart Structures and Materials and Nondestructive Evaluation and Health Monitoring, San Diego, California.Google Scholar
10. Dalton, R.P., Cawley, P. and Lowe, M.J.S., The potential of guided waves for monitoring large areas of metallic aircraft fuselage structure, J Nondestructive Evaluation, 2001, 20, pp 29–46.Google Scholar
11. Lee, B.C. and Staszewski, W.J., Modelling of Lamb waves for damage detection in metallic structures: Part I — Wave propagation, Smart Mater Struct, 2003, 12, pp. 804–814.Google Scholar
12. Eren, L. and Devaney, M.J., Bearing damage detection via wavelet packet decomposition of the stator current, IEEE Transactions on Instrumentation and Measurement, 2004, 53, pp 431–436.Google Scholar
13. Park, G., Rutherford, A.C., Sohn, H. and Farrar, C.R., An outlier analysis framework for impedance-based structural health monitoring, J Sound and Vibration, 2005, 286, pp 229–250.Google Scholar
14. Sohn, H., Allen, D.W., Worden, K. and Farrar, C.R., Structural damage classification using extreme value statistics, J Dynamic Systems, Measurement, and Control, 2005, 127, pp 125–132.Google Scholar
15. Lee, J.W., Kirikera, G.R., Kang, I., Schulz, M.J. and Shanov, V. N., Structural health monitoring using continuous sensors and neural network analysis, Smart Mater Struct, 2006, 15, pp 1266–1274.Google Scholar
16. Cohen, L., Time-frequency distributions — A review, Proceedings of the IEEE, 1989, 77, pp 941–981.Google Scholar
17. Mallat, S.G., A Wavelet Tour of Signal Processing, Second edition, 1998, Academic Press.Google Scholar
18. Papandreou-Suppappola, A. (Ed). Applications in Time-Frequency Signal Processing, 2002, CRC Press, Florida.Google Scholar
19. Luo, C., Wei, J., Garcia, M., Chattopadhyay, A. and Peralta, P., A multiscale model for fatigue damage prediction in metallic materials, 2008, 49th AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics and Materials Conference, Schaumburg, Illinois.Google Scholar
20. Hill, R., Generalized constitutive relations for incremental deformation of metal crystals by multislip, J Mech Phys Solids, 1966, 14, p 95.Google Scholar
21. Rice, J.R., Inelastic constitutive relations for solids: An internal-variable theory and its application to metal plasticity, J Mech Phys Solids, 1971, 19, p 433.Google Scholar
22. Hill, R. and Rice, J.R., Constitutive analysis of elastic-plastic crystals at arbitrary strain, J Mech Phys Solids, 1972, 20, p 401.Google Scholar
23. Asaro, R.J. and Rice, J.R., Strain localization in ductile single crystals, J Mech Phys Solids, 1977, 25, p 309.Google Scholar
24. Asaro, R.J., Micromechanics of crystals and polycrystals, Adv Appl Mech, 1983, 23, p 1.Google Scholar
25. Asaro, R.J., Crystal plastictiy, J Appl Mech, 1983, 50, p 921.Google Scholar
26. Jiang, Y., A fatigue criterion for general multiaxial loading, Fat Frac Eng Mat Struct, 2000, 23, p 19.Google Scholar
27. Kovvali, N., Das, S., Chakraborty, D., Cochran, D., Papandreou-Suppapola, A. and Chattopadhyay, A., Time-frequency based classification of structural damage, 2007, 48th AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference, pp 2047–2055, Honolulu, Hawaii.Google Scholar
28. Zhou, W., Kovvali, N., Papandreou-Suppappola, A., Cochran, D. and Chattopadhyay, A., Hidden Markov model based classification of structural damage, Proc of SPIE, 2007, 6523, p 652311.Google Scholar
29. Zhou, W., Chakraborty, D., Kovvali, N., Papandreou-Suppappola, A., Cochran, D. and Chattopadhyay, A., Damage classification for structural health monitoring using time-frequency feature extraction and continuous hidden Markov models, 2007, Asilomar Conference on Signals, Systems, and Computers, pp 848–852, Pacific Grove, California.Google Scholar
30. Channels, L., Chakraborty, D., Simon, D., Kovvali, N., Spicer, J., Papandreou-Suppappola, A., Cochran, D., Peralta, P. and Chattopadhyay, A., Ultrasonic sensing and time-frequency analysis for detecting plastic deformation in an aluminum plate, Proc of SPIE, 2008, 6926, p 69260P.Google Scholar
31. Chakraborty, D., Soni, S., Wei, J., Kovvali, N., Papandreou-Suppappola, A., Cochran, D. and Chattopadhyay, A., Physics based modeling for time-frequency damage classification, Proc of SPIE, 2008, 6926, p 69260M.Google Scholar
32. Zhou, W., Reynolds, W.D., Moncada, A., Kovvali, N., Chattopadhyay, A., Papandreou-Suppappola, A. and Cochran, D., Sensor fusion and damage classification in composite materials, Proc of SPIE, 2008, 6926, p 69260N.Google Scholar
33. Chakraborty, D., Kovvali, N., Wei, J., Papandreou-Suppappola, A., Cochran, D. and Chattopadhyay, A., Damage classification for structural health monitoring using timefrequency techniques, J Intelligent Material Systems and Structures, Special issue on Structural Health Monitoring, July 2009, 20, pp 1289–1305.Google Scholar
34. Zhou, W., Kovvali, N., Papandreou-Suppappola, A., Cochran, D., Chattopadhyay, A. and Reynolds, W., Classification of damage in composite structures using hidden Markov models for integrated vehicle health management, J Intelligent Material Systems and Structures, Special issue on Structural Health Monitoring, July 2009, 20, pp 1271–1288.Google Scholar
35. Chakraborty, D., Zhou, W., Simon, D., Kovvali, N., Papandreou-Suppappola, A., Cochran, D. and Chattopadhyay, A., Time-frequency methods for structural health monitoring, 2008, Sensor, Signal and Information Processing (SenSIP) Workshop.Google Scholar
36. Mallat, S.G. and Zhang, Z., Matching pursuits with time-frequency dictionaries, IEEE Transactions on Signal Processing, 1993, 41, pp 3397–3415.Google Scholar
37. Bharadwaj, P.K., Runkle, P. and Carin, L., Target identification with wave-based matched pursuits and hidden Markov models, IEEE Transactions on Antennas and Propagation, 1999, 47, pp 1543–1554.Google Scholar
38. Ebenezer, S.P., Classification of Time-varying Signals from an Acoustic Monitoring System using Time-frequency Techniques, December 2001, Master’s thesis, Arizona State University, Tempe, AZ.Google Scholar
39. Ebenezer, S.P., Papandreou-Suppappola, A. and Suppappola, S.B., Matching pursuit classification for time-varying acoustic emissions, 2001, 35th Asilomar Conference on Signals, Systems and Computers, pp 715–719.Google Scholar
40. Ebenezer, S.P., Papandreou-Suppappola, A. and Suppappola, S.B., Classification of acoustic emissions using modified matching pursuit, EURASIP J Applied Signal Processing, 2004, 3, pp 347–357.Google Scholar
41. Rabiner, L.R. and Juang, B.H., An introduction to hidden Markov models, IEEE ASSP Magazine, 1986, pp 4–15.Google Scholar
42. Rabiner, L.R., A tutorial on hidden Markov models and selected applications in speech recognition, 1989, Proceedings of the IEEE, 77, pp 257–286.Google Scholar
43. Duda, R.O., Hart, P.E. and Stork, D.G., Pattern Classification, Second edition, 2001, Wiley Interscience.Google Scholar
44. MacKay, D.J.C. Information Theory, Inference, and Learning Algorithms, 2003, Cambridge University Press.Google Scholar
45. Beal, M.J., Variational Algorithms for Approximate Bayesian Inference, 2003, PhD thesis, Gatsby Computational Neuroscience Unit, University College London.Google Scholar
46. MacKay, D.J.C., Ensemble learning for hidden Markov models, 1997, Technical report, Cavendish Laboratory, University of Cambridge.Google Scholar
47. Mohanty, S., Das, S., Chattopadhyay, A. and Peralta, P., Gaussian process time series model for life prognosis of metallic structure, J Intelligent Material Systems and Structures, to appear.Google Scholar
48. Mohanty, S., Chattopadhyay, A., Peralta, P., Das, S. and Willhauck, C., Fatigue life prediction using multivariate Gaussian process, 2008, AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics and Materials Conference, Schaumburg, Illinois, 2008.Google Scholar
49. MacKay, D.J.C., Introduction to Gaussian processes, Neural Networks and Machine Learning, Bishop, C.M. (Ed), 1998, pp 133–165, Springer, Berlin.Google Scholar
50. Rasmussen, C. and Williams, C., Gaussian Processes for Machine Learning, 2006, The MIT Press, Cambridge, MA, USA.Google Scholar