A data-driven method for predicting structural degradation using a piezoceramic array
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Published
Nov 1, 2020
Kyle R Mulligan
Chunsheng Yang
Nicolas Quaegebeur
Patrice Masson
Abstract
There is a growing use of carbon fiber reinforced polymers (CFRPs) in modern airframes with still a limited understanding of the in-service behavioral characteristics of these structures.
Structural Health Monitoring (SHM) technologies that use surface-bonded piezoceramic (PZT) transducers to generate and measure guided waves within these structures have demonstrated promising damage detection and localization results and potential for data gathering in data-driven damage prognosis. This paper investigates the development of a data-driven SHM based damage prognosis system for estimating remaining useful life (RUL) of CFRP coupons following damage initiation. A robust and realistic laboratory data gathering methodology is introduced as a building block for evaluating the feasibility of data-driven damage prognosis for in-service aerospace structures. Data are gathered using a PZT-based SHM system. Using the gathered raw guided wave signals, a number of time and frequency domain features are first extracted which are derived from existing damage imaging and detection algorithms. Then, using various combinations of the feature sets as inputs to generic data mining algorithms, the paper presents estimates of the predicted RUL against actual damage diameter progression.
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Keywords
SHM, PZT, data-driven, damage prognosis, data mining and machine learning, drop-weight impacting, composite
References
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Chang, F., & Chang, K. (1987). A progressive damagemodel for laminated composites containing stress concentrations. Journal of Composite Materials, 21(9), 834–855.
Chattopadhyay, A., Papandreou-Suppapola, A., Kim, B. K., Kovvali, N., Farrar, C. R., Triplett, M. H., & Derriso, M. M. (2012). Workshop on transitioning structural health monitoring technology to military platforms (Tech. Rep.). DTIC.
Chen, M.-S., Han, J., & Yu, P. S. (1996). Data mining: an overview from a database perspective. IEEE Transactions on Knowledge and Data Engineering, 8(6), 866–883.
Chiachio, J., Chiachio,M., Saxena, A., Rus, G., & Goebel, K. (2013). Based prognostic framework to predict fatigue damage evolution in composites. In Proceedings of the annueal conference of the prognostics and health management society, new orleans, la, usa, 14-17 october.
Chiachio, M., Chiachio, J., Saxena, A., Rus, G., & Goebel, K. (2013). Fatigue damage prognosis in frp composites by combining multi- scale degradation fault modes in an uncertainty Bayesian framework. In Proceedings of the 9th international workshop on structural health monitoring, stanford, ca, usa, 10-12 september.
Choi, H. (1990). Damage in graphite/epoxy laminated composites due to low-velocity impact (Unpublished doctoral dissertation). Stanford University.
Croxford, A. J., Moll, J., Wilcox, P. D., & Michael, J. E. (2010). Efficient temperature compensation strategies for guided wave structural health monitoring. Ultrasonics, 50(4-5), 517–528.
Giurgiutiu, V., & Bao, J. J. (2004). Embedded-ultrasonics structural radar for in situ structural health monitoring of thin-wall structures. Structural Health Monitoring, 3(2), 121–140.
Guida, M., Marulo, F., Meo, M., & Russo, S. (2012). Certification by birdstrike analysis on c27j fullscale ribless composite leading edge,. International Journal of Impact Engineering, doi:10.1016/j.ijimpeng.2012.10.002.
Hall, M. (2000). Correlation-based feature selection for discrete and numeric class machine learning. In Proceedings of the 17th international conference on machine learning.
Hashin, Z. (1980). Failure criteria for unidirectional fiber composites. Journal of Applied Mechanics, 32(1), 1921–1944.
Hou, J. P., Petrinic, N., Ruiz, C., & Hallett, S. R. (2000). Prediction of impact damage in composite plates. Composites Science and Technology, 60(2), 228–273.
Kira, K., & Rendell, L. (1992). A practical approach to feature selection. In Proceedings of the 9th international conference on machine learning.
Konstantinidis, G., Wilcox, P. D., & Drinkwater, B. W. (2007). An investigation into the temperature stability of a guided wave structural health monitoring system using permanently attached sensors. IEEE Sensors Journal, 7(5), 905–912.
Larrosa, C., & Chang, F.-K. (2011). Characterization of matrix micro-cracking in composite laminates using builtin piezo-electric sensors. In Annual conference of the prognostics and health management society, montreal , qc, canada, 25-29 september.
Létourneau, S., Yang, C., Drummond, C., Scarlett, E., Valdés, J., & Zaluski, M. (2005). A domain independent data mining methodology for prognostics. In Essential technologies for successful prognostics: proceedings of the 59th meeting of the society for machinery failure prevention technology, virginia beach, virginia, 18-21 april.
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Mulligan, K. R., Masson, P., Létourneau, S., & Quaegebeur, N. (2011). An approach to compensate for the degradation of the monitoring system in damage detection. In Proceedings of the Canadian Institute for NDE.
Mulligan, K. R., Ostiguy, P.-C., Masson, P., Elkoun, S., & Quaegebeur, N. (2011). Assessment of PZT transducer bonding techniques under drop-weight impact loading in composites. In Proceedings of spie.
Mulligan, K. R., Quaegebeur, N., Masson, P., Brault, L.- P., & Yang, C. (2013). Compensation of piezoceramic bonding layer degradation for structural health monitoring. Structural Health Monitoring, doi:10.1177/1475921713500516.
Mulligan, K. R., Quaegebeur, N., Masson, P., & Létourneau, S. (2012). Correction of data gathered by degraded transducers for damage prognosis in composite structures. In Annual conference of the prognostics health management society.
Mulligan, K. R., Quaegebeur, N., Ostiguy, P.-C., Masson, P., & Létourneau, S. (2013). Comparison of metrics to monitor and compensate for piezoceramic degradation in structural health monitoring. StructuralHealthMonitoring, 12(2), 153–168.
Nairn, J. A. (Ed.). (2000). Matrix microcracking in composites. Elsevier.
Nguyen,M. Q., Jacombs, S. S., Thomson, R. S., Hachenberg, D., & Scott, M. L. (2005). Simulation of impact on sandwich structures. Composite Structures, 67, 217–227.
O’Brien, T. K. (Ed.). (2001). Fracture mechanics of composite delamination - asm handbook - composites. ASM International.
Olympus. (2013). Ultrasonic flaw detectors-omniscan mx2 (Tech. Rep.). Author.
Ostiguy, P.-C., Quaegebeur, N., Mulligan, K. R., Masson, P., & Elkoun, S. (2012). In-situ mechanical characterization of isotropic structures using guided wave propagation. Smart Materials and Structures, 21(1), 1-9.
Park, S., Park, G., Yun, C.-B., & Farrar, C. R. (2009). Sensor self-diagnosis using a modified impedance model for active sensing-based structural health monitoring. Structural Health Monitoring, 8(1), 71–82.
Quaegebeur, N., Masson, P., Langlois-Demers, D., & Micheau, P. (2010). Dispersion–based imaging for structural health monitoring using sparse and compact arrays. Smart Materials and Structures, 20(1), 1–12.
Quaegebeur, N.,Masson, P.,Micheau, P., &Mrad, N. (2012). Broadband generation of ultrasonic guidedwaves using sub-band decomposition. IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, 59(5), 928–938.
Rhymer, J., Kim, H., & Roach, D. (2012). The damage resistance of quasi-isotropic carbon-epoxy composite tape laminates impacted by high velocity ice. Composites: Part A, 43(7), 1134–1144.
Roebuck, B., Gorley, T. A. E., & McCartney, L. N. (1989). Mechanical property test procedures for metal matrix composites. Materials Science and Technology, 5(2), 105–117.
Roemer, M. J., Ge, J., & Liberson, A. (2005). Autonomous impact damage detection and isolation prediction for aerospace structures. In Proceedings of ieee aerospace conference, big sky, mt, usa, 5-12 march.
Saxena, A., Goebel, K., Larrosa, C. C., Janapati, V., & Roy, S. (2011). Accelerated aging experiments for prognostics of damage growth in composite materials. In Proceedings of the 8th internationa workshop on structural health monitoring, stanford, ca, usa, 13-15 sept.
Soutis, C. (2005). Fibre reinforced composites in aircraft construction. Progress in Aerospace Sciences, 41(2), 143–151.
Staszewski, W. J., Mahzan, S., & Traynor, R. (2009). Health monitoring of aerospace composite structures - active and passive approach. Composites Science and Technology, 69(11–12), 1678–1685.
Tomblin, J., Lacy, T., Smith, B., Hooper, S., Vizzini, A., & Lee, S. (August 1999). Review of damage tolerance for composite sandwich airframe structures (Tech. Rep. No. DOT/FAA/AR-99/49). Federal Aviation Administration, Office of Aviation Research,Washington, DC.
Wilcox, P., Lowe, M., & Cawley, P. (2001). The effect if dispersion on long-range inspection using ultrasonic guided waves. NDT&E international, 34(1), 1–9.
Witten, I. H., & Frank, E. (Eds.). (2005). Data mining: Practical machine learning tools and techniques, 2nd edition. Morgan Kaufmann, San Francisco.
Zaluski, M., Létourneau, S., Bird, J., & Yang, C. (2010). Developing data mining-based prognostic models for cf-18 aircraft. In Proceedings of asme turbo expo 2010: Power for land, sea and air, glascow, uk, 14-18 june.
Beaumont, P. W. R., Diamant, R. A., & Shercliff, H. R. (2006). Failure processes in composite materials: getting physical. Journal of Material Science, 41(20), 6526–6546.
Chang, F., & Chang, K. (1987). A progressive damagemodel for laminated composites containing stress concentrations. Journal of Composite Materials, 21(9), 834–855.
Chattopadhyay, A., Papandreou-Suppapola, A., Kim, B. K., Kovvali, N., Farrar, C. R., Triplett, M. H., & Derriso, M. M. (2012). Workshop on transitioning structural health monitoring technology to military platforms (Tech. Rep.). DTIC.
Chen, M.-S., Han, J., & Yu, P. S. (1996). Data mining: an overview from a database perspective. IEEE Transactions on Knowledge and Data Engineering, 8(6), 866–883.
Chiachio, J., Chiachio,M., Saxena, A., Rus, G., & Goebel, K. (2013). Based prognostic framework to predict fatigue damage evolution in composites. In Proceedings of the annueal conference of the prognostics and health management society, new orleans, la, usa, 14-17 october.
Chiachio, M., Chiachio, J., Saxena, A., Rus, G., & Goebel, K. (2013). Fatigue damage prognosis in frp composites by combining multi- scale degradation fault modes in an uncertainty Bayesian framework. In Proceedings of the 9th international workshop on structural health monitoring, stanford, ca, usa, 10-12 september.
Choi, H. (1990). Damage in graphite/epoxy laminated composites due to low-velocity impact (Unpublished doctoral dissertation). Stanford University.
Croxford, A. J., Moll, J., Wilcox, P. D., & Michael, J. E. (2010). Efficient temperature compensation strategies for guided wave structural health monitoring. Ultrasonics, 50(4-5), 517–528.
Giurgiutiu, V., & Bao, J. J. (2004). Embedded-ultrasonics structural radar for in situ structural health monitoring of thin-wall structures. Structural Health Monitoring, 3(2), 121–140.
Guida, M., Marulo, F., Meo, M., & Russo, S. (2012). Certification by birdstrike analysis on c27j fullscale ribless composite leading edge,. International Journal of Impact Engineering, doi:10.1016/j.ijimpeng.2012.10.002.
Hall, M. (2000). Correlation-based feature selection for discrete and numeric class machine learning. In Proceedings of the 17th international conference on machine learning.
Hashin, Z. (1980). Failure criteria for unidirectional fiber composites. Journal of Applied Mechanics, 32(1), 1921–1944.
Hou, J. P., Petrinic, N., Ruiz, C., & Hallett, S. R. (2000). Prediction of impact damage in composite plates. Composites Science and Technology, 60(2), 228–273.
Kira, K., & Rendell, L. (1992). A practical approach to feature selection. In Proceedings of the 9th international conference on machine learning.
Konstantinidis, G., Wilcox, P. D., & Drinkwater, B. W. (2007). An investigation into the temperature stability of a guided wave structural health monitoring system using permanently attached sensors. IEEE Sensors Journal, 7(5), 905–912.
Larrosa, C., & Chang, F.-K. (2011). Characterization of matrix micro-cracking in composite laminates using builtin piezo-electric sensors. In Annual conference of the prognostics and health management society, montreal , qc, canada, 25-29 september.
Létourneau, S., Yang, C., Drummond, C., Scarlett, E., Valdés, J., & Zaluski, M. (2005). A domain independent data mining methodology for prognostics. In Essential technologies for successful prognostics: proceedings of the 59th meeting of the society for machinery failure prevention technology, virginia beach, virginia, 18-21 april.
Liu, Y., Mohanty, S., & Chattopadhyay, A. (2009). A gaussian process based prognostics framework for composite structures. In Proceedings of spie, san diego, ca, usa, 8-12 march.
Mulligan, K. R., Masson, P., Létourneau, S., & Quaegebeur, N. (2011). An approach to compensate for the degradation of the monitoring system in damage detection. In Proceedings of the Canadian Institute for NDE.
Mulligan, K. R., Ostiguy, P.-C., Masson, P., Elkoun, S., & Quaegebeur, N. (2011). Assessment of PZT transducer bonding techniques under drop-weight impact loading in composites. In Proceedings of spie.
Mulligan, K. R., Quaegebeur, N., Masson, P., Brault, L.- P., & Yang, C. (2013). Compensation of piezoceramic bonding layer degradation for structural health monitoring. Structural Health Monitoring, doi:10.1177/1475921713500516.
Mulligan, K. R., Quaegebeur, N., Masson, P., & Létourneau, S. (2012). Correction of data gathered by degraded transducers for damage prognosis in composite structures. In Annual conference of the prognostics health management society.
Mulligan, K. R., Quaegebeur, N., Ostiguy, P.-C., Masson, P., & Létourneau, S. (2013). Comparison of metrics to monitor and compensate for piezoceramic degradation in structural health monitoring. StructuralHealthMonitoring, 12(2), 153–168.
Nairn, J. A. (Ed.). (2000). Matrix microcracking in composites. Elsevier.
Nguyen,M. Q., Jacombs, S. S., Thomson, R. S., Hachenberg, D., & Scott, M. L. (2005). Simulation of impact on sandwich structures. Composite Structures, 67, 217–227.
O’Brien, T. K. (Ed.). (2001). Fracture mechanics of composite delamination - asm handbook - composites. ASM International.
Olympus. (2013). Ultrasonic flaw detectors-omniscan mx2 (Tech. Rep.). Author.
Ostiguy, P.-C., Quaegebeur, N., Mulligan, K. R., Masson, P., & Elkoun, S. (2012). In-situ mechanical characterization of isotropic structures using guided wave propagation. Smart Materials and Structures, 21(1), 1-9.
Park, S., Park, G., Yun, C.-B., & Farrar, C. R. (2009). Sensor self-diagnosis using a modified impedance model for active sensing-based structural health monitoring. Structural Health Monitoring, 8(1), 71–82.
Quaegebeur, N., Masson, P., Langlois-Demers, D., & Micheau, P. (2010). Dispersion–based imaging for structural health monitoring using sparse and compact arrays. Smart Materials and Structures, 20(1), 1–12.
Quaegebeur, N.,Masson, P.,Micheau, P., &Mrad, N. (2012). Broadband generation of ultrasonic guidedwaves using sub-band decomposition. IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, 59(5), 928–938.
Rhymer, J., Kim, H., & Roach, D. (2012). The damage resistance of quasi-isotropic carbon-epoxy composite tape laminates impacted by high velocity ice. Composites: Part A, 43(7), 1134–1144.
Roebuck, B., Gorley, T. A. E., & McCartney, L. N. (1989). Mechanical property test procedures for metal matrix composites. Materials Science and Technology, 5(2), 105–117.
Roemer, M. J., Ge, J., & Liberson, A. (2005). Autonomous impact damage detection and isolation prediction for aerospace structures. In Proceedings of ieee aerospace conference, big sky, mt, usa, 5-12 march.
Saxena, A., Goebel, K., Larrosa, C. C., Janapati, V., & Roy, S. (2011). Accelerated aging experiments for prognostics of damage growth in composite materials. In Proceedings of the 8th internationa workshop on structural health monitoring, stanford, ca, usa, 13-15 sept.
Soutis, C. (2005). Fibre reinforced composites in aircraft construction. Progress in Aerospace Sciences, 41(2), 143–151.
Staszewski, W. J., Mahzan, S., & Traynor, R. (2009). Health monitoring of aerospace composite structures - active and passive approach. Composites Science and Technology, 69(11–12), 1678–1685.
Tomblin, J., Lacy, T., Smith, B., Hooper, S., Vizzini, A., & Lee, S. (August 1999). Review of damage tolerance for composite sandwich airframe structures (Tech. Rep. No. DOT/FAA/AR-99/49). Federal Aviation Administration, Office of Aviation Research,Washington, DC.
Wilcox, P., Lowe, M., & Cawley, P. (2001). The effect if dispersion on long-range inspection using ultrasonic guided waves. NDT&E international, 34(1), 1–9.
Witten, I. H., & Frank, E. (Eds.). (2005). Data mining: Practical machine learning tools and techniques, 2nd edition. Morgan Kaufmann, San Francisco.
Zaluski, M., Létourneau, S., Bird, J., & Yang, C. (2010). Developing data mining-based prognostic models for cf-18 aircraft. In Proceedings of asme turbo expo 2010: Power for land, sea and air, glascow, uk, 14-18 june.
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Technical Papers