Get This Paper

Advanced Condition-Based Maintenance of Composites Based on Real-Time Electromechanical Behavior Data


Title: Advanced Condition-Based Maintenance of Composites Based on Real-Time Electromechanical Behavior Data

Authors: In-Yong Lee, Soyoung Oh and Young-Bin Park

DOI: 10.33599/nasampe/c.22.0105

Abstract: Studies on real-time structural health monitoring (SHM) and prognostics and health management (PHM) for composite structures using self-sensing data are limited. For reducing the unexpected failure, maintenance cost and maximize the life cycle of composites structures, study for CBM+ which is consisted with SHM and PHM is needed. This study proposes an advanced real-time condition-based maintenance methodology for composites under cyclic stress condition based on their electromechanical behavior. The electrical resistance of carbon fiber composite structures is measured under cyclic stress condition such as repeated impacts test. The electromechanical behavior is investigated, and the various damage types in the composite structures are analyzed during multiple impacts testing using designed data analysis system by comparing the electromechanical behavior with material property. PHM algorithms are proposed in this study to predict the electromechanical behavior of a composite during cyclic test using the particle filter. Furthermore, remaining useful property was calculated in real-time with few number of historical data. Based on SHM and PHM analysis, this study introduces a real-time condition-based maintenance methodology for efficient system maintenance by combining SHM and PHM, using real-time self-sensing data. The applicability of the method was verified by using it to assess the impact damage on wind turbine blade.

References: [1] Dai, Gaoming, and Leon Mishnaevsky Jr. ""Carbon nanotube reinforced hybrid composites: computational modeling of environmental fatigue and usability for wind blades."" Composites Part B: Engineering 78 (2015): 349-360 [2] Katnam, K. B., et al. ""Composite repair in wind turbine blades: an overview."" The Journal of Adhesion 91.1-2 (2015): 113-139. [3] Watson, James C., and Juan C. Serrano. ""Composite materials for wind blades."" Wind Syst. Mag 46 (2010): 46-51. [4] Cantwell, W. J., and J. Morton. ""Detection of impact damage in CFRP laminates."" Composite Structures 3.3-4 (1985): 241-257. [5] Shen, Qin, Mohammed Omar, and Shan Dongri. ""Ultrasonic NDE techniques for impact damage inspection on CFRP laminates."" Journal of materials science research 1.1 (2012): 2. [6] Tsuda, H., Toyama, N., Urabe, K., & Takatsubo, J. (2004). Impact damage detection in CFRP using fiber Bragg gratings. Smart Materials and Structures, 13(4), 719. [7] Li, Yin, et al. ""Low-velocity impact damage characterization of carbon fiber reinforced polymer (CFRP) using infrared thermography."" Infrared Physics & Technology 76 (2016): 91-102. [8] Todoroki, Akira, et al. ""Impact damage detection of a carbon-fibre-reinforced-polymer plate employing self-sensing time-domain reflectometry."" Composite structures 130 (2015): 174-179. [9] Roh, Hyung Doh, So Young Oh, and Young-Bin Park. ""Self-sensing impact damage in and non-destructive evaluation of carbon fiber-reinforced polymers using electrical resistance and the corresponding electrical route models."" Sensors and Actuators A: Physical 332 (2021): 112762. [10] Wen, Sihai, and D. D. L. Chung. ""Electrical-resistance-based damage self-sensing in carbon fiber reinforced cement."" Carbon 45.4 (2007): 710-716. [11] Roh, Hyung Doh, Homin Lee, and Young-Bin Park. ""Structural health monitoring of carbon-material-reinforced polymers using electrical resistance measurement."" International Journal of Precision Engineering and Manufacturing-Green Technology 3.3 (2016): 311-321. [12] Roh, Hyung Doh, et al. ""Deformation and interlaminar crack propagation sensing in carbon fiber composites using electrical resistance measurement."" Composite Structures 216 (2019): 142-150. [13] Likas, Aristidis, Nikos Vlassis, and Jakob J. Verbeek. ""The global k-means clustering algorithm."" Pattern recognition 36.2 (2003): 451-461. [14] Kodinariya, Trupti M., and Prashant R. Makwana. ""Review on determining number of Cluster in K-Means Clustering."" International Journal 1.6 (2013): 90-95. [15] Kanungo, Tapas, et al. ""An efficient k-means clustering algorithm: Analysis and implementation."" IEEE transactions on pattern analysis and machine intelligence 24.7 (2002): 881-892. [16] Abdi, Hervé, and Lynne J. Williams. ""Principal component analysis."" Wiley interdisciplinary reviews: computational statistics 2.4 (2010): 433-459. [17] Ringnér, Markus. ""What is principal component analysis?."" Nature biotechnology 26.3 (2008): 303-304. [18] Paul, Liton Chandra, Al A. Suman, and Nahid Sultan. ""Methodological analysis of principal component analysis (PCA) method."" International Journal of Computational Engineering & Management 16.2 (2013): 32-38. [19] Gustafsson, Fredrik. ""Particle filter theory and practice with positioning applications."" IEEE Aerospace and Electronic Systems Magazine 25.7 (2010): 53-82. [20] Chopin, Nicolas. ""A sequential particle filter method for static models."" Biometrika 89.3 (2002): 539-552. [21] Jouin, Marine, et al. ""Particle filter-based prognostics: Review, discussion and perspectives."" Mechanical Systems and Signal Processing 72 (2016): 2-31. [22] Elenchezhian, Muthu Ram Prabhu, et al. ""Artificial intelligence in real-time diagnostics and prognostics of composite materials and its uncertainties—A review."" Smart Materials and Structures 30.8 (2021): 083001. [23] Liu, Huan, et al. ""Prognostics of damage growth in composite materials using machine learning techniques."" 2017 IEEE International Conference on Industrial Technology (ICIT). IEEE, 2017. [24] Lee, In Yong, et al. ""Advanced non-destructive evaluation of impact damage growth in carbon-fiber-reinforced plastic by electromechanical analysis and machine learning clustering."" Composites Science and Technology 218 (2022): 109094. [25] Lee, In Yong, Hyung Doh Roh, and Young-Bin Park. ""Prediction method for propagating crack length of carbon-fiber-based composite double cantilever beam using its electromechanical behavior and particle filter."" Composite Structures 279 (2022): 114650. [26] Lee, In Yong Hyung Doh Roh, and Young-Bin Park, “Condition-Based Monitoring with Prognostics of Composite Structures Under Multiple Impacts Using Electromechanical Behavior Data with a Particle Filter” Available at SSRN:

Conference: CAMX 2022

Publication Date: 2022/10/17

SKU: TP22-0000000105

Pages: 11

Price: $22.00

Get This Paper