[1] Fang, K., Zhou, Y., Wang, S., Ye, R., & Guo, S. (2018). Assessing national renewable energy competitiveness of the G20: A revised Porter’s Diamond Model. Renewable and Sustainable Energy Reviews, 93, 719-731.
[2] Chou, J.-S., Chiu, C.-K., Huang, I.-K., & Chi, K.- N. (2013). Failure analysis of wind turbine blade under critical wind loads. Engineering Failure Analysis, 27, 99-118.
[3] Liu, W., Tang, B., Han, J., Lu, X., Hu, N., & He, Z. (2015). The structure healthy condition monitoring and fault diagnosis methods in wind turbines: A review. Renewable and Sustainable Energy Reviews, 44, 466-472.
[4] Yang, R., He, Y., & Zhang, H. (2016). Progress and trends in nondestructive testing and evaluation for wind turbine composite blade. Renewable and Sustainable Energy Reviews, 60, 1225-1250.
[5] Lin, Y., Tu, L., Liu, H., & Li, W. (2016). Fault analysis of wind turbines in China. Renewable and Sustainable Energy Reviews, 55, 482-490.
[6] Meng, H., Lien, F.-S., & Li, L. (2018). Elastic actuator line modelling for wake-induced fatigue analysis of horizontal axis wind turbine blade. Renewable Energy, 116, 423- 437.
[7] Ciang, C. C., Lee, J.-R., & Bang, H.-J. (2008). Structural health monitoring for a wind turbine system: A review of damage detection methods. Measurement Science and Technology, 19(12), 122001.
[8] Ackermann, T., & Söder, L. (2000). Wind energy technology and current status: A review. Renewable and Sustainable Energy Reviews, 4(4), 315-374.
[9] Garolera, A. C., Madsen, S. F., Nissim, M., Myers, J. D., & Holboell, J. (2014). Lightning damage to wind turbine blades from wind farms in the US. IEEE Transactions on Power Delivery, 31(3), 1043-1049.
[10] Sørensen, B. F., Joergensen, E., Debel, C. P., Jensen, H. M., Jacobsen, T. K., & Halling, K. (2004). Improved design of large wind turbine blade of fibre composites based on studies of scale effects (Phase 1). Summary report.
[11] Schubel, P., Crossley, R., Boateng, E., & Hutchinson, J. (2013). Review of structural health and cure monitoring techniques for large wind turbine blades. Renewable Energy, 51, 113-123.
[12] Ye, X., Su, Y., & Han, J. (2014). Structural health monitoring of civil infrastructure using optical fiber sensing technology: A comprehensive review. The Scientific World Journal, 2014.
[13] Hameed, Z., Hong, Y., Cho, Y., Ahn, S., & Song, C. (2009). Condition monitoring and fault detection of wind turbines and related algorithms: A review. Renewable and Sustainable Energy Reviews, 13(1), 1-39.
[14] Ramakrishnan, M., Rajan, G., Semenova, Y., & Farrell, G. (2016). Overview of fiber optic sensor technologies for strain/temperature sensing applications in composite materials. Sensors, 16(1), 99.
[15] Nair, A., & Cai, C. (2010). Acoustic emission monitoring of bridges: Review and case studies. Engineering Structures, 32(6), 1704- 1714.
[16] Qiao, W., & Lu, D. (2015). A survey on wind turbine condition monitoring and fault diagnosis—Part II: Signals and signal processing methods. IEEE Transactions on Industrial Electronics, 62(10), 6546-6557.
[17] Blanch, M., & Dutton, A. (2003). Acoustic emission monitoring of field tests of an operating wind turbine. Key Engineering Materials, 245, 475-482.
[18] Bo, Z., Yanan, Z., & Changzheng, C. (2017). Acoustic emission detection of fatigue cracks in wind turbine blades based on blind deconvolution separation. Fatigue & Fracture of Engineering Materials & Structures, 40(6), 959-970.
[19] Li, X., Yang, Z., & Chen, X. (2014). Quantitative damage detection and sparse sensor array optimization of carbon fiber reinforced resin composite laminates for wind turbine blade structural health monitoring. Sensors, 14(4), 7312-7331.
[20] Jørgensen, E. R., Borum, K. K., McGugan, M., Thomsen, C., Debel, C., & Sørensen, B. F. (2004). Full scale testing of wind turbine blade to failure-flapwise loading.
[21] Tchakoua, P., Wamkeue, R., Tameghe, T. A., & Ekemb, G. (2013). A review of concepts and methods for wind turbines condition monitoring. In 2013 World Congress on
Computer and Information Technology (WCCIT) (pp. 1-9). IEEE. [22] Katnam, K., Comer, A., Roy, D., Da Silva, L., & Young, T. (2015). Composite repair in wind turbine blades: An overview. The Journal of Adhesion, 91(1-2), 113-139.
[23] Hernandez Crespo, B. (2016). Damage sensing in blades. MARE-WINT: New Materials and Reliability in Offshore Wind Turbine Technology, 25-52.
[24] Park, B., An, Y.-K., & Sohn, H. (2014). Visualization of hidden delamination and debonding in composites through noncontact laser ultrasonic scanning. Composites Science and Technology, 100, 10-18.
[27] Ye, G., Neal, B., Boot, A., Kappatos, V., Selcuk, C., & Gan, T.-H. (2014). Development of an ultrasonic NDT system for automated in-situ inspection of wind turbine blades. In EWSHM-7th European Workshop on Structural Health Monitoring.
[26] Habibi, H., Cheng, L., Zheng, H., Kappatos, V., Selcuk, C., & Gan, T.-H. (2015). A dual deicing system for wind turbine blades combining high-power ultrasonic guided waves and low-frequency forced vibrations. Renewable Energy, 83, 859-870.
[27] Jiménez, A. A., Muñoz, C. Q. G., & Márquez, F. P. G. (2019). Dirt and mud detection and diagnosis on a wind turbine blade employing guided waves and supervised learning
classifiers. Reliability Engineering & System Safety, 184, 2-12. [28] Sohn, H., Dutta, D., Yang, J., DeSimio, M., Olson, S., & Swenson, E. (2011). Automated detection of delamination and disbond from wavefield images obtained using a scanning laser vibrometer. Smart Materials and Structures, 20(4), 045017.
[29] Yan, Y., Cheng, L., Wu, Z., & Yam, L. (2007). Development in vibration-based structural damage detection technique. Mechanical Systems and Signal Processing, 21(5), 2198- 2211.
[30] Amezquita-Sanchez, J. P., & Adeli, H. (2016). Signal processing techniques for vibrationbased health monitoring of smart structures. Archives of Computational Methods in Engineering, 23, 1-15.
[31] Dervilis, N., et al. (2014). On damage diagnosis for a wind turbine blade using pattern recognition. Journal of Sound and Vibration, 333(6), 1833-1850.
[32] Skrimpas, G. A., Kleani, K., Mijatovic, N., Sweeney, C. W., Jensen, B. B., & Holboell, J. (2016). Detection of icing on wind turbine blades by means of vibration and power curve analysis. Wind Energy, 19(10), 1819- 1832.
[33] Ulriksen, M. D., Tcherniak, D., Kirkegaard, P. H., & Damkilde, L. (2016). Operational modal analysis and wavelet transformation for damage identification in wind turbine blades. Structural Health Monitoring, 15(4), 381-388.
[34] Oliveira, G., Magalhães, F., Cunha, Á., & Caetano, E. (2018). Vibration-based damage detection in a wind turbine using 1 year of data. *Structural Control and Health Monitoring*, 25(11), e2238.
[35] Colone, L., Hovgaard, M., Glavind, L., & Brincker, R. (2018). Mass detection, localization and estimation for wind turbine blades based on statistical pattern recognition. *Mechanical Systems and Signal Processing*, 107, 357-374.
[36] Jin, X., Jiang, C., Li, Y., Yang, S., & Qiao, G. (2018). Health monitoring of composite materials: A review of current and future trends. Composite Structures, 196, 166-185.
[37] Amani, M., Nejad, M. F., & Sadoughi, A. (2015). An experimental study on the effect of mechanical properties of wind turbine blades on the fatigue life. Journal of Wind Engineering and Industrial Aerodynamics, 146, 303-311.
[38] Wang, Y., Wang, H., Huang, Y., & Wang, X. (2019). Condition monitoring and fault diagnosis of wind turbine blades based on vibration and temperature signals: A review. Renewable and Sustainable Energy Reviews, 100, 273-285.
[39] Cheng, L., & Chen, Y. (2018). Review on vibration-based structural health monitoring of wind turbine blades. *Engineering Structures*, 163, 372-387.
[40] Qiao, Y., Sun, L., & Wang, C. (2019). A review of vibration-based damage detection in wind turbine blades. Structural Control and Health Monitoring, 26(4), e2271.
[41] Sun, S., & Chen, C. (2018). Time-frequency analysis of wind turbine blade vibrations using the wavelet transform. Mechanical Systems and Signal Processing, 99, 177-191.
[42] Liu, H., Chen, Q., Zhang, J., & Sun, L. (2017). Damage detection in wind turbine bladesusing vibration signals based on neural networks. Energy, 132, 145-155.
[43] Hu, X., Li, Q., & Qian, Z. (2016). A comprehensive review of the state of health monitoring methods for wind turbine blades. *Applied Energy*, 179, 1090-1103.
[44] Huang, J., Yang, Y., & Zhang, Y. (2015). Research on the structural health monitoring of wind turbine blades: A review. Renewable and Sustainable Energy Reviews, 52, 88-103.
[45] Costa, J. L., & Pina, A. C. (2018). Nondestructive testing and monitoring techniques for wind turbine blades: A review. NDT & E International, 98, 35-47.
[46] Adhikari, R., & Bhatia, K. (2017). Structural health monitoring of wind turbine blades using acoustic emission and vibration techniques: A review. Smart Structures and Systems, 19(2), 219-234.
[47] Wang, Y., & Wu, D. (2017). A review of structural health monitoring of wind turbine blades using vibration-based methods. Journal of Wind Engineering and Industrial Aerodynamics, 170, 290-306.
[48] Zhan, C., Liu, J., & Zhang, H. (2019). Review of piezoelectric-based health monitoring of wind turbine blades. Sensors, 19(1), 135.
[49] Yang, M., & Wang, L. (2019). Vibrationbased structural health monitoring of wind turbine blades using a multi-sensor approach. Mechanical Systems and Signal Processing, 126, 359-372.
[50] Adeli, H., & Jiang, J. (2016). Smart structures and systems: Technology and applications. Computational Mechanics, 58(1), 19-32.
[51] Montalvo, A., & Gonzalez, R. (2018). Applications of machine learning in wind turbine condition monitoring: A review. Energy Reports, 4, 554-560.
[52] Zhang, Y., Xiong, J., Wang, X., & Liu, J. (2016). Recent advances in condition monitoring and fault diagnosis of wind turbines: A review. Renewable and Sustainable Energy Reviews, 62, 267-280.
[53] Chen, C., Chen, L., & Chang, Y. (2017). A review of wind turbine blade condition monitoring methods based on vibration signals. Structural Control and Health Monitoring, 24(3), e1944.
[54] Stojanovic, J., & Ostoja-Starzewski, M. (2018). Health monitoring of wind turbine blades: Review and future directions. Wind Energy, 21(9), 1039-1052.
[55] Zhao, X., Wang, F., & Li, J. (2017). Review on the applications of fiber optic sensors for health monitoring of wind turbine blades. Sensors, 17(12), 2741.
[56] Hu, G., & Wang, H. (2018). A review on condition monitoring of wind turbine blades using infrared thermography. Renewable and Sustainable Energy Reviews, 82, 2066-2078.
[57] Liao, Y., Wang, S., & Yang, Y. (2017). A review of recent developments in condition monitoring and fault diagnosis of wind turbine blades. Mechanical Systems and Signal Processing, 86, 547-567.
