Lamb-wave-based nondestructive testing and evaluation (NDT/E) methods have drawn much attention due to their potential to inspect plate-like structures in a variety of industrial applications. To estimate and/or predict fatigue crack growth, many research efforts have been made to develop data-driven or physics-based methods. Data-driven methods show high predictive capability without the need for physical domain knowledge; however, fewer data can lead to overfitting in the results. On the other hand, physics-based methods can provide reliable results without the need for measured data; however, small amounts of physical information can worsen their predictive capability. In real applications, both the measurable data and the physical information of systems may be considerably limited; it is thus challenging to estimate and/or predict the crack length using either the data-driven or physics-based method alone. To make use of the advantages and minimize the disadvantages of each method, the work outlined in this paper aims to develop a hybrid approach that combines the data-driven and the physics-based methods for estimation and prediction of fatigue crack growth with and without Lamb wave signals. First, with Lamb wave signals, a data-driven method based on signal processing and the random forest model can be used estimate crack lengths. Second, in the absence of Lamb wave signals, a physics-based method based on an ensemble prognostics approach and Walker’s equation can be used to predict crack lengths with the help of the previously estimated crack lengths. To demonstrate the validity of the proposed approach, a case study is presented using datasets provided in the 2019 PHM Conference Data Challenge by the PHM Society. The case study confirms that the proposed method shows high accuracy; the RMSEs for specimens T7 and T8 are calculated as 0.2021 and 0.551, respectively. A penalty score is calculated as 7.63; this result led to a 2nd place finish in the Data Challenge. To the best of the authors’ knowledge, this is the first attempt to propose a hybrid approach for estimation and prediction of fatigue crack growth.
fatigue crack growth, prediction, Estimation, Hybrid Approach
Benkedjouh, T., Medjaher, K., Zerhouni, N., & Rechak, S. (2013). Remaining useful life estimation based on nonlinear feature reduction and support vector regression. Engineering Applications of Artificial Intelligence, 26(7), 1751-1760.
Büyüköztürk, O., & Taşdemir, M. A. (2012). Nondestructive Testing of Materials and Structures: Springer Science & Business Media.
Cawley, P., & Alleyne, D. (1996). The use of Lamb waves for the long range inspection of large structures. Ultrasonics, 34(2-5), 287-290.
Ha, J. M., Youn, B. D., Oh, H., Han, B., Jung, Y., & Park, J. (2016). Autocorrelation-based time synchronous averaging for condition monitoring of planetary gearboxes in wind turbines. Mechanical Systems and Signal Processing, 70, 161-175.
He, J., Ran, Y., Liu, B., Yang, J., & Guan, X. (2017). A fatigue crack size evaluation method based on lamb wave simulation and limited experimental data. Sensors, 17(9), 2097.
Jeon, B. C., Jung, J. H., Youn, B. D., Kim, Y.-W., & Bae, Y.-C. (2015). Datum unit optimization for robustness of a journal bearing diagnosis system. international journal of precision engineering and manufacturing, 16(11), 2411-2425.
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.
Le Clézio, E., Castaings, M., & Hosten, B. (2002). The interaction of the S0 Lamb mode with vertical cracks in an aluminium plate. Ultrasonics, 40(1-8), 187-192.
Maslouhi, A. (2011). Fatigue crack growth monitoring in aluminum using acoustic emission and acousto‐ultrasonic methods. Structural control and health monitoring, 18(7), 790-806.
Mohanty, S., Chattopadhyay, A., Peralta, P., & Das, S. (2011). Bayesian statistic based multivariate Gaussian process approach for offline/online fatigue crack growth prediction. Experimental mechanics, 51(6), 833-843.
Oh, H., Jung, J. H., Jeon, B. C., & Youn, B. D. (2017). Scalable and unsupervised feature engineering using vibration-imaging and deep learning for rotor system diagnosis. IEEE Transactions on Industrial Electronics, 65(4), 3539-3549.
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., . . . Dubourg, V. (2011). Scikit-learn: Machine learning in Python. Journal of machine learning research, 12(Oct), 2825-2830.
Rolfe, S. T., & Barsom, J. M. (1977). Fracture and fatigue control in structures: Applications of fracture mechanics: ASTM International.
Staszewski, W., Lee, B., & Traynor, R. (2007). Fatigue crack detection in metallic structures with Lamb waves and 3D laser vibrometry. Measurement science and technology, 18(3), 727.
Wang, H., Zhang, W., Sun, F., & Zhang, W. (2017). A comparison study of machine learning based algorithms for fatigue crack growth calculation. Materials, 10(5), 543.
Wilcox, P. D. (1998). Lamb wave inspection of large structures using permanently attached transducers. University of London.
Yashiro, S., Takatsubo, J., & Toyama, N. (2007). An NDT technique for composite structures using visualized Lamb-wave propagation. Composites Science and Technology, 67(15-16), 3202-3208.
Yu, L., & Liu, H. (2003). Feature selection for high-dimensional data: A fast correlation-based filter solution. Paper presented at the Proceedings of the 20th international conference on machine learning (ICML-03).