Sensor Networks for Structures Health Monitoring: Placement, Implementations, and Challenges—A Review
Abstract
:1. Introduction
2. Design Requirements for Sensor Networks
3. Modelling and Optimization for Sensor Network Design
3.1. Sensors for Measuring Vibration
3.2. Sensors for Measuring Strain
3.3. Sensor for Measuring Ultrasonic Wave
3.4. A Case Study for Sensor Network Optimization—PZT Wafers
4. Commercial and Academic Sensor Systems
4.1. Commercial Systems
4.2. Prototypes—Academics’ Contribution
5. Sensor Networks—Testing, Fault, and Robustness
6. Hardware Development for Sensor Integration
7. Data Communication and Acquisition Systems
7.1. Acquisition System
7.2. Data Communication Technologies
8. Energy Sources for Sensors
8.1. Energy Storage
8.2. Energy Harvesting
8.3. Energy Management
9. Feature Extraction and Signal Processing
9.1. Vibration Measurement
- (1).
- Modal Parameters
- (2).
- Time-Domain Data
- (3).
- Frequency-Domain Data
9.2. Ultrasonic Wave Data
10. Case Studies—Laboratory
11. Cases Studies—Implementations in the Field
12. Advantages and Challenges of the Technology
13. Conclusions
Author Contributions
Funding
Conflicts of Interest
References
- Lynch, J.P.; Loh, K.J. A summary review of wireless sensors and sensor networks for structural health monitoring. Shock Vib. Dig. 2006, 38, 91–130. [Google Scholar] [CrossRef] [Green Version]
- Runcie, P.; Mustapha, S.; Rakotoarivelo, T. Advances in structural health monitoring system architecture. In Proceedings of the Fourth International Symposium on Life-Cycle Civil Engineering, IALCCE, Tokyo, Japan, 16–19 November 2014; pp. 1064–1071. [Google Scholar]
- Sohn, H.; Farrar, C.R.; Hemez, F.M.; Shunk, D.D.; Stinemates, D.W.; Nadler, B.R.; Czarnecki, J.J. A review of structural health monitoring literature: 1996–2001. In Proceedings of the Third World Conference on Structural Control, Como, Italy, 7–12 April 2002. [Google Scholar]
- Ou, J.; Li, H. Structural health monitoring in mainland China: Review and future trends. Struct. Health Monit. 2010, 9, 219–231. [Google Scholar]
- Patil, P.K.; Patil, S.R. Review on structural health monitoring system using WSN for bridges. In Proceedings of the 2017 International Conference of Electronics, Communication and Aerospace Technology (ICECA), Coimbatore, India, 20–22 April 2017; pp. 628–631. [Google Scholar]
- Ostachowicz, W.; Soman, R.; Malinowski, P. Optimization of sensor placement for structural health monitoring: A review. Struct. Health Monit. 2019, 18, 963–988. [Google Scholar] [CrossRef]
- Ismail, Z.; Mustapha, S.; Fakih, M.A.; Tarhini, H. Sensor placement optimization on complex and large metallic and composite structures. Struct. Health Monit. 2020, 19, 262–280. [Google Scholar] [CrossRef]
- Su, Z.; Ye, L.; Lu, Y. Guided Lamb waves for identification of damage in composite structures: A review. J. Sound Vib. 2006, 295, 753–780. [Google Scholar] [CrossRef]
- Mustapha, S.; Braytee, A.; Ye, L. Multisource Data Fusion for Classification of Surface Cracks in Steel Pipes. J. Nondestruct. Eval. Diagn. Progn. Eng. Syst. 2018, 1. [Google Scholar] [CrossRef]
- Anaissi, A.; Khoa, N.L.D.; Mustapha, S.; Alamdari, M.M.; Braytee, A.; Wang, Y.; Chen, F. Adaptive One-Class Support Vector Machine for Damage Detection in Structural Health Monitoring. In Proceedings of the Pacific-Asia Conference on Knowledge Discovery and Data Mining, Jeju, Korea, 23–26 May 2017; pp. 42–57. [Google Scholar]
- Yang, J.; He, J.; Guan, X.; Wang, D.; Chen, H.; Zhang, W.; Liu, Y. A probabilistic crack size quantification method using in-situ Lamb wave test and Bayesian updating. Mech. Syst. Signal Process. 2016, 78, 118–133. [Google Scholar] [CrossRef]
- Dong, W.; Lin, Y.; Zhongqing, S.; Ye, L.; Fucai, L.; Guang, M. Probabilistic Damage Identification Based on Correlation Analysis Using Guided Wave Signals in Aluminum Plates. Struct. Health Monit. 2009, 9, 133–144. [Google Scholar] [CrossRef]
- Mustapha, S.; Hu, Y.; Nguyen, K.; Alamdari, M.M.; Runcie, P.; Dackermann, U.; Nguyen, V.; Li, J.; Ye, L. Pattern Recognition Based on Time Series Analysis Using Vibration Data for Structural Health Monitoring in Civil Structures. Electron. J. Struct. Eng. 2015, 14, 106–115. [Google Scholar]
- Doebling, S.W.; Farrar, C.R.; Prime, M.B. A summary review of vibration-based damage identification methods. Shock Vib. Dig. 1998, 30, 91–105. [Google Scholar] [CrossRef] [Green Version]
- Yeager, M.; Todd, M.; Gregory, W.; Key, C. Assessment of embedded fiber Bragg gratings for structural health monitoring of composites. Struct. Health Monit. 2016, 16, 262–275. [Google Scholar] [CrossRef]
- Mustapha, S.; Ye, L.; Dong, X.; Alamdari, M.M. Evaluation of barely visible indentation damage (BVID) in CF/EP sandwich composites using guided wave signals. Mech. Syst. Signal Process. 2016, 76–77, 497–517. [Google Scholar] [CrossRef]
- Tang, X.; Wang, X.; Cattley, R.; Gu, F.; Ball, D.A. Energy Harvesting Technologies for Achieving Self-Powered Wireless Sensor Networks in Machine Condition Monitoring: A Review. Sensors 2018, 18, 4113. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Chen, B.; Wang, X.; Sun, D.; Xie, X. Integrated System of Structural Health Monitoring and Intelligent Management for a Cable-Stayed Bridge. Sci. World J. 2014, 2014, 12. [Google Scholar] [CrossRef]
- Gutierrez, J.A.; Callaway, E.H.; Barrett, R. IEEE 802.15.4 Low-Rate Wireless Personal Area Networks: Enabling Wireless Sensor Networks; IEEE Standards: Middlesex County, NJ, USA, 2003. [Google Scholar]
- Marcelloni, F.; Vecchio, M. A Simple Algorithm for Data Compression in Wireless Sensor Networks. IEEE Commun. Lett. 2008, 12, 411–413. [Google Scholar] [CrossRef]
- Zou, Z.; Bao, Y.; Deng, F.; Li, H. An Approach of Reliable Data Transmission with Random Redundancy for Wireless Sensors in Structural Health Monitoring. IEEE Sens. J. 2015, 15, 809–818. [Google Scholar] [CrossRef]
- Mahajan, U.; Prashanth, C. Algorithms for data compression in wireless computing systems. Int. J. Comput. Sci. Issues 2013, 10, 71. [Google Scholar]
- Blilat, A.; Bouayad, A.; Chaoui, N.E.H.; Ghazi, M.E. Wireless sensor network: Security challenges. In 2012 National Days of Network Security and Systems; IEEE: Marrakech, Morocco, 2012. [Google Scholar]
- Jiang, X.; Tang, Y.; Lei, Y. Wireless sensor networks in Structural Health Monitoring based on ZigBee technology. In 2009 3rd International Conference on Anti-counterfeiting, Security, and Identification in Communication; IEEE: Hong Kong, China, 2009. [Google Scholar]
- Alonso, L.; Barbarán, J.; Chen, J.; Díaz, M.; Llopis, L.; Rubio, B. Middleware and communication technologies for structural health monitoring of critical infrastructures: A survey. Comput. Stand. Interfaces 2018, 56, 83–100. [Google Scholar] [CrossRef]
- Jeong, S.; Byun, J.; Kim, D.; Sohn, H.; Bae, I.H.; Law, K.H. A data management infrastructure for bridge monitoring. In Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2015; International Society for Optics and Photonics: Bellingham, DC, USA, 2015; p. 94350P. [Google Scholar]
- Kammer, D.C. Sensor placement for on-orbit modal identification and correlation of large space structures. J. Guid. Control Dyn. 1991, 14, 251–259. [Google Scholar] [CrossRef]
- Trendafilova, I.; Heylen, W.; Brussel, H.V. Measurement point selection in damage detection using the mutual information concept. Smart Mater. Struct. 2001, 10, 528–533. [Google Scholar] [CrossRef]
- Worden, K.; Burrows, A. Optimal sensor placement for fault detection. Eng. Struct. 2001, 23, 885–901. [Google Scholar] [CrossRef]
- Malinowski, P.H.; Ostachowicz, W.M.; Brune, K.; Schlag, M. Study of electromechanical impedance changes caused by modifications of CFRP adhesive bonds. Fatigue Fract. Eng. Mater. Struct. 2017, 40, 1592–1600. [Google Scholar] [CrossRef] [Green Version]
- Wandowski, T.; Malinowski, P.H.; Ostachowicz, W.M. Temperature and damage influence on electromechanical impedance method used for carbon fibre–reinforced polymer panels. J. Intell. Mater. Syst. Struct. 2017, 28, 782–798. [Google Scholar] [CrossRef]
- Glassburn, R.S.; Smith, S.W. Evaluation of Sensor Placement Algorithms for On-Orbit Identification of Space Platforms. Master’s Thesis, University of Kentucky, Lexington, KY, USA, 1994. [Google Scholar]
- Jin, S.; Zhou, M.; Wu, A.S. Sensor network optimization using a genetic algorithm. In Proceedings of the 7th World Multiconference on Systemics, Cybernetics and Informatics, Orlando, FL, USA, 27–30 July 2003; pp. 109–116. [Google Scholar]
- Mallardo, V.; Sharif Khodaei, Z.; Aliabadi, F.M. A Bayesian approach for sensor optimisation in impact identification. Materials 2016, 9, 946. [Google Scholar] [CrossRef]
- Flynn, E.B.; Todd, M.D. Optimal placement of piezoelectric actuators and sensors for detecting damage in plate structures. J. Intell. Mater. Syst. Struct. 2010, 21, 265–274. [Google Scholar] [CrossRef]
- Mallardo, V.; Aliabadi, M.; Khodaei, Z.S. Optimal sensor positioning for impact localization in smart composite panels. J. Intell. Mater. Syst. Struct. 2013, 24, 559–573. [Google Scholar] [CrossRef]
- Mallardo, V.; Aliabadi, M. Optimal sensor placement for structural, damage and impact identification: A review. Struct. Durab. Health Monit. 2013, 9, 287–323. [Google Scholar] [CrossRef]
- Croxford, A.J.; Wilcox, P.D.; Drinkwater, B.W. Quantification of sensor geometry performance for guided wave SHM. In Health Monitoring of Structural and Biological Systems 2009; International Society for Optics and Photonics: Bellingham, DC, USA, 2009; p. 72951H. [Google Scholar]
- Guo, H.; Zhang, L.; Zhang, L.; Zhou, J. Optimal placement of sensors for structural health monitoring using improved genetic algorithms. Smart Mater. Struct. 2004, 13, 528. [Google Scholar] [CrossRef] [Green Version]
- Yi, T.H.; Li, H.N.; Wang, C.W. Multiaxial sensor placement optimization in structural health monitoring using distributed wolf algorithm. Struct. Control Health Monit. 2016, 23, 719–734. [Google Scholar] [CrossRef]
- Thiene, M.; Khodaei, Z.S.; Aliabadi, M. Optimal sensor placement for maximum area coverage (MAC) for damage localization in composite structures. Smart Mater. Struct. 2016, 25, 095037. [Google Scholar] [CrossRef]
- Cantero-Chinchilla, S.; Chiachío, J.; Chiachío, M.; Chronopoulos, D.; Jones, A. A robust Bayesian methodology for damage localization in plate-like structures using ultrasonic guided-waves. Mech. Syst. Signal Process. 2019, 122, 192–205. [Google Scholar] [CrossRef] [Green Version]
- Manohar, K.; Brunton, B.W.; Kutz, J.N.; Brunton, S.L. Data-Driven Sparse Sensor Placement for Reconstruction: Demonstrating the Benefits of Exploiting Known Patterns. IEEE Control Syst. 2018, 38, 63–86. [Google Scholar]
- Zhang, H.; Hou, J.C. On the upper bound of α-lifetime for large sensor networks. ACM Trans. Sens. Netw. (TOSN) 2005, 1, 272–300. [Google Scholar] [CrossRef] [Green Version]
- Salmanpour, M.; Sharif Khodaei, Z.; Aliabadi, M. Transducer placement optimisation scheme for a delay and sum damage detection algorithm. Struct. Control Health Monit. 2017, 24, e1898. [Google Scholar] [CrossRef] [Green Version]
- Cantero-Chinchilla, S.; Chiachío, J.; Chiachío, M.; Chronopoulos, D.; Jones, A. Optimal sensor configuration for ultrasonic guided-wave inspection based on value of information. Mech. Syst. Signal Process. 2020, 135, 106377. [Google Scholar] [CrossRef]
- Cantero-Chinchilla, S.; Beck, J.L.; Chiachío, M.; Chiachío, J.; Chronopoulos, D.; Jones, A. Optimal sensor and actuator placement for structural health monitoring via an efficient convex cost-benefit optimization. Mech. Syst. Signal Process. 2020, 144, 106901. [Google Scholar] [CrossRef]
- Shoja, S.; Berbyuk, V.; Mustapha, S. Design optimization of transducer arrays for uniform distribution of guided wave energy in arbitrarily shaped domains. Ultrasonics 2020, 103, 106079. [Google Scholar] [CrossRef] [PubMed]
- Tarhini, H.; Itani, R.; Fakih, M.A.; Mustapha, S. Optimization of piezoelectric wafer placement for structural health-monitoring applications. J. Intell. Mater. Syst. Struct. 2018, 29, 3758–3773. [Google Scholar] [CrossRef]
- Airbus, S. Airbus A380 Aircraft Characteristics Airport and Maintenance Planning; Airbus S.A.S: Blagnac, France, 2015. [Google Scholar]
- Zhou, C.; Su, Z.; Cheng, L. Probability-based diagnostic imaging using hybrid features extracted from ultrasonic Lamb wave signals. Smart Mater. Struct. 2011, 20, 125005. [Google Scholar] [CrossRef]
- Wu, Z.; Liu, K.; Wang, Y.; Zheng, Y. Validation and evaluation of damage identification using probability-based diagnostic imaging on a stiffened composite panel. J. Intell. Mater. Syst. Struct. 2014, 26, 2181–2195. [Google Scholar] [CrossRef]
- BINDT—NDT Equipment Providers. Available online: https://www.bindt.org/Buyers-Guide/NDT-Equipment-Providers/ (accessed on 1 July 2021).
- NI. Compatible Sensors and Sensor Suppliers for NI Wireless Sensor Network. 2018. Available online: http://www.ni.com/product-documentation/9998/en/ (accessed on 12 June 2019).
- MicroStrain, L.S. Wireless Sensor Nodes. Available online: https://www.microstrain.com/wireless/nodes (accessed on 12 June 2019).
- OMEGA. Learn More about Wireless Sensors. Available online: https://www.omega.com/en-us/resources/wireless-sensors#section1 (accessed on 13 June 2019).
- ADVANTECH. Wireless Sensing Solutions. Available online: https://www.advantech.com/products/wireless-sensing-network/sub_3f18fa5c-7506-47e1-8043-494956f0aff6 (accessed on 13 June 2019).
- Qing, X.P.; Beard, S.J.; Kumar, A.; Ooi, T.K.; Chang, F.-K. Built-in Sensor Network for Structural Health Monitoring of Composite Structure. J. Intell. Mater. Syst. Struct. 2007, 18, 39–49. [Google Scholar] [CrossRef]
- Wu, Z.; Qing, X.P.; Chang, F.-K. Damage Detection for Composite Laminate Plates with A Distributed Hybrid PZT/FBG Sensor Network. J. Intell. Mater. Syst. Struct. 2009, 20, 1069–1077. [Google Scholar] [CrossRef]
- Guo, Z.; Kim, K.; Salowitz, N.; Lanzara, G.; Wang, Y.; Peumans, P.; Chang, F.-K. Functionalization of stretchable networks with sensors and switches for composite materials. Struct. Health Monit. 2018, 17, 598–623. [Google Scholar] [CrossRef] [Green Version]
- Qiang, W.; Shenfang, Y. Baseline-free Imaging Method based on New PZT Sensor Arrangements. J. Intell. Mater. Syst. Struct. 2009, 20, 1663–1673. [Google Scholar] [CrossRef]
- Bao, Q.; Yuan, S.; Guo, F.; Qiu, L. Transmitter beamforming and weighted image fusion–based multiple signal classification algorithm for corrosion monitoring. Struct. Health Monit. 2019, 18, 621–634. [Google Scholar] [CrossRef]
- Quek, S.T.; Jin, J.; Tua, P.S. Comparison of Plain Piezoceramics and Interdigital Transducers for Crack Detection in Plates; SPIE: Bellingham, DC, USA, 2004; Volume 5390. [Google Scholar]
- Wang, D.; Ye, L.; Lu, Y.; Li, F. A damage diagnostic imaging algorithm based on the quantitative comparison of Lamb wave signals. Smart Mater. Struct. 2010, 19, 065008. [Google Scholar] [CrossRef]
- Fasel, T.R.; Todd, M.D. An adhesive bond state classification method for a composite skin-to-spar joint using chaotic insonification. J. Sound Vib. 2010, 329, 3218–3232. [Google Scholar] [CrossRef]
- Gardiner, G. Structural health monitoring: NDT-integrated aerostructures. In SHM Moves from Structural Testing to an FAA-Qualified Inspection Alternative, to Reduce Cost, Streamline Operations and Mature toward Lighter, More Robust Smart Structures; Composites World: Cincinnati, OH, USA, 2015. [Google Scholar]
- Michaels, J.E. Detection, localization and characterization of damage in plates with anin situarray of spatially distributed ultrasonic sensors. Smart Mater. Struct. 2008, 17, 035035. [Google Scholar] [CrossRef] [Green Version]
- Rao, A.R.M.; Kasireddy, V.; Gopalakrishnan, N.; Lakshmi, K. Sensor fault detection in structural health monitoring using null subspace–based approach. J. Intell. Mater. Syst. Struct. 2015, 26, 172–185. [Google Scholar] [CrossRef]
- Alcaide, A.; Barrera, E.; Ruiz, M.; Aranguren, G. Damage detection on Aerospace structures using PAMELA SHM System. In Proceedings of the 6th International Symposium on NDT in Aerospace, Madrid, Spain, 12–14 November 2014. [Google Scholar]
- Aranguren, G.; Monje, P.M.; Cokonaj, V.; Barrera, E.; Ruiz, M. Ultrasonic wave-based structural health monitoring embedded instrument. Rev. Sci. Instrum. 2013, 84, 125106. [Google Scholar] [CrossRef]
- Ostachowicz, W.; Kudela, P.; Malinowski, P.; Wandowski, T. Damage localisation in plate-like structures based on PZT sensors. Mech. Syst. Signal Process. 2009, 23, 1805–1829. [Google Scholar] [CrossRef]
- Kudela, P.; Ostachowicz, W.; Żak, A. Damage detection in composite plates with embedded PZT transducers. Mech. Syst. Signal Process. 2008, 22, 1327–1335. [Google Scholar] [CrossRef]
- Stepinski, T.; Engholm, M. Piezoelectric Circular Array for Structural Health Monitoring Using Lamb Waves. In Proceedings of the 7th International Workshop on Structural Health Monitoring, Stanford, CA, USA, 9–11 September 2009; Volume 1, pp. 1050–1056. [Google Scholar]
- Giurgiutiu, V. Structural Health Monitoring: With Piezoelectric Wafer Active Sensors; Elsevier: Amsterdam, The Netherlands, 2007. [Google Scholar]
- Yu, L.; Santoni-Bottai, G.; Xu, B.; Liu, W.; Giurgiutiu, V. Piezoelectric wafer active sensors for in situ ultrasonic-guided wave SHM. Fatigue Fract. Eng. Mater. Struct. 2008, 31, 611–628. [Google Scholar] [CrossRef]
- Pena, J.; Melguizo, C.; Martinez-Ona, R.; Ullate, Y.; de Espinosa Freijo, F.; Kawiecki, G. Advanced phased array system for structural damage detection. In Proceedings of the Third European Workshop on Structural Health Monitoring, Granada, Spain, 5–7 July 2006; pp. 244–250. [Google Scholar]
- Sundararaman, S.; Adams, D.E.; Rigas, E.J. Biologically inspired structural diagnostics through beamforming with phased transducer arrays. Int. J. Eng. Sci. 2005, 43, 756–778. [Google Scholar] [CrossRef]
- Criado, A.; Melguizo, C.P.; Macias, J.P.; Martinez–Ona, R.; Kawiecki, G. Proceedings of the III ECCOMAS Thematic Conference on Smart Structures and Materials, Gdańsk, Poland, 9–11 July 2007.
- Yu, L.; Giurgiutiu, V. In situ 2-D piezoelectric wafer active sensors arrays for guided wave damage detection. Ultrasonics 2008, 48, 117–134. [Google Scholar] [CrossRef]
- Malinowski, P.; Wandowski, T.; Trendafilova, I.; Ostachowicz, W.M. Multi-phased array for damage localisation. In Key Engineering Materials; Trans Tech Publications Ltd.: Freienbach, Switzerland, 2007; pp. 77–82. [Google Scholar]
- Wilcox, P.D. Omni-directional guided wave transducer arrays for the rapid inspection of large areas of plate structures. IEEE Trans. Ultrason. Ferroelectr. Freq. Control 2003, 50, 699–709. [Google Scholar] [CrossRef]
- Engholm, M.; Stepinski, T. Using 2-D Arrays for Sensing Multimodal Lamb Waves; SPIE: Bellingham, DC, USA, 2010; Volume 7649. [Google Scholar]
- Jaussaud, G.; Rebufa, J.; Fournier, M.; Logeais, M.; Bencheikh, N.; Rébillat, M.; Guskov, M. Improving Lamb Wave Detection for SHM Using a Dedicated LWDS Electronics; NDT.net: Paris, France, 2019. [Google Scholar]
- Ni, Y.Q.; Xia, Y.; Liao, W.Y.; Ko, J.M. Technology innovation in developing the structural health monitoring system for Guangzhou New TV Tower. Struct. Control Health Monit. 2009, 16, 73–98. [Google Scholar] [CrossRef]
- Wong, K.-Y. Instrumentation and health monitoring of cable-supported bridges. Struct. Control Health Monit. 2004, 11, 91–124. [Google Scholar] [CrossRef]
- Ko, J.M.; Ni, Y.Q. Technology developments in structural health monitoring of large-scale bridges. Eng. Struct. 2005, 27, 1715–1725. [Google Scholar] [CrossRef]
- Mascarenas, D.L.; Todd, M.D.; Park, G.; Farrar, C.R. Development of an impedance-based wireless sensor node for structural health monitoring. Smart Mater. Struct. 2007, 16, 2137. [Google Scholar] [CrossRef] [Green Version]
- Chae, M.J.; Yoo, H.S.; Kim, J.Y.; Cho, M.Y. Development of a wireless sensor network system for suspension bridge health monitoring. Autom. Constr. 2012, 21, 237–252. [Google Scholar] [CrossRef]
- Cho, S.; Yun, C.B.; Lynch, J.P.; Zimmerman, A.T.; Spencer, B.F.; Nagayama, T. Smasrt wireless sensor technology for structural health monitoring of civil structures. Steel Struct. 2008, 8, 267–275. [Google Scholar]
- Park, H.S.; Shin, Y.; Choi, S.W.; Kim, Y. An Integrative Structural Health Monitoring System for the Local/Global Responses of a Large-Scale Irregular Building under Construction. Sensors 2013, 13, 9085–9103. [Google Scholar] [CrossRef] [Green Version]
- Mustapha, S.; Kassir, A.; Hassoun, K.; Modad, B.A.A.; Abi-Rached, H.; Dawy, Z. Joint Crowd Management and Structural Health Monitoring Using Fiber Optic and Wearable Sensing. IEEE Commun. Mag. 2019, 57, 62–67. [Google Scholar] [CrossRef]
- Dener, M. WiSeN: A new sensor node for smart applications with wireless sensor networks. Comput. Electr. Eng. 2017, 64, 380–394. [Google Scholar] [CrossRef]
- Fepeussi, T.V.; Jin, Y.; Xu, Y.; Xiang, D.; Huo, F. Acoustic data communication by wireless sensor network on plate-like structures for autonomous structural health monitoring aerovehicles. In Autonomous Systems: Sensors, Vehicles, Security, and the Internet of Everything; International Society for Optics and Photonics: Bellingham, DC, USA, 2018. [Google Scholar]
- Kane, M.B.; Peckens, C.; Lynch, J.P. Design and selection of wireless structural monitoring systems for civil infrastructures. In Sensor Technologies for Civil Infrastructures; Wang, M.L., Lynch, J.P., Sohn, H., Eds.; Woodhead Publishing: Sawston, UK, 2014; Volume 55, pp. 446–479. [Google Scholar]
- Hung, S.L.; Ding, J.T.; Lu, Y.C. Developing an energy-efficient and low-delay wake-up wireless sensor network-based structural health monitoring system using on-site earthquake early warning system and wake-on radio. J. Civ. Struct. Health Monit. 2019, 9, 103–115. [Google Scholar] [CrossRef]
- Yuan, S.; Ren, Y.; Qiu, L.; Mei, H. A multi-response-based wireless impact monitoring network for aircraft composite structures. IEEE Trans. Ind. Electron. 2016, 63, 7712–7722. [Google Scholar] [CrossRef]
- Chen, J.; Li, P.; Song, G.; Tan, Y.; Zheng, Y.; Han, Y. Systematic development of a wireless snesor netowrk for piezo-based sensing. J. Sens. 2018, 2018, 12. [Google Scholar] [CrossRef]
- Spencer, B.F.; Park, J.W.; Mechitov, K.A.; Jo, H.; Agha, G. Next Generation Wireless Smart Sensors Toward Sustainable Civil Infrastructure. Procedia Eng. 2017, 171, 5–13. [Google Scholar] [CrossRef]
- Wang, H.; Dong, L.; Wei, W.; Zhao, W.; Xu, K.; Wang, G. The WSN Monitoring System for Large Outdoor Advertising Boards Based on ZigBee and MEMS Sensor. IEEE Sens. J. 2018, 18, 1314–1323. [Google Scholar] [CrossRef]
- Zhao, X.; Wei, G.; Li, X.; Qin, Y.; Xu, D.; Tang, W.; Yin, H.; Wei, X.; Jia, L. Self-powered triboelectric nano vibration accelerometer based wireless sensor system for railway state health monitoring. Nano Energy 2017, 34, 549–555. [Google Scholar] [CrossRef] [Green Version]
- Lee, J.-H.; Jung, J.-E.; Kim, N.-G.; Song, B.-H. Industrial Pipe-Rack Health Monitoring System Based on Reliable-Secure Wireless Sensor Network. Int. J. Distrib. Sens. Netw. 2012, 8, 641391. [Google Scholar] [CrossRef]
- Oraczewski, T.; Staszewski, W.J.; Uhl, T. Nonlinear acoustics for structural health monitoring using mobile, wireless and smartphone-based transducer platform. J. Intell. Mater. Syst. Struct. 2016, 27, 786–796. [Google Scholar] [CrossRef]
- Mukhopadhyay, S.C. Power Supplies for Sensors. In Intelligent Sensing, Instrumentation and Measurements; Springer: Berlin/Heidelberg, Germany, 2013; pp. 71–90. [Google Scholar]
- Park, G.; Rosing, T.; Todd, M.D.; Farrar, C.R.; Hodgkiss, W. Energy Harvesting for Structural Health Monitoring Sensor Networks. J. Infrastruct. Syst. 2008, 14, 64–79. [Google Scholar] [CrossRef] [Green Version]
- Sampaio, P.G.V.; González, M.O.A. Photovoltaic solar energy: Conceptual framework. Renew. Sustain. Energy Rev. 2017, 74, 590–601. [Google Scholar] [CrossRef]
- Jung, H.J.; Kim, I.H.; Park, J. Experimental validation of energy harvesting device for cicivl engineering applicaitons. In Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2012; International Society for Optics and Photonics: Bellingham, DC, USA, 2012; p. 8345C. [Google Scholar]
- Lagomarsini, C.; Kachroudi, A.; Basrous, S.; Jean-Mistral, C.; Sylvestre, A. Autonomous electrostatic generator for energy harvesting applications under inertial load. In Electroactive Polymer Actuators and Devices (EAPAD) XX; International Society for Optics and Photonics: Bellingham, DC, USA, 2018. [Google Scholar]
- Cahill, P.; Mathewson, A.; Pakrashi, V. Experimental Validation of Piezoelectric Energy-Harvesting Device for Built Infrastructure Applications. J. Bridge Eng. 2018, 23, 04018056. [Google Scholar] [CrossRef]
- Farinholt, K.M.; Miller, N.; Sifuentes, W.; MacDonald, J.; Park, G.; Farrar, C.R. Energy harvesting and wireless energy transmission for embedded SHM sensor nodes. Struct. Health Monit. 2010, 9, 269–280. [Google Scholar] [CrossRef]
- Lu, X.; Wang, P.; Niyato, D.; Kim, D.I.; Han, Z. Wireless Networks With RF Energy Harvesting: A Contemporary Survey. IEEE Commun. Surv. Tutor. 2015, 17, 757–789. [Google Scholar] [CrossRef] [Green Version]
- Srbinovski, B.; Magno, M.; Edwards-Murphy, F.; Pakrashi, V.; Popovici, E. An Energy Aware Adaptive Sampling Algorithm for Energy Harvesting WSN with Energy Hungry Sensors. Sensors 2016, 16, 448. [Google Scholar] [CrossRef] [Green Version]
- Dürager, C.; Heinzelmann, A.; Riederer, D. A wireless sensor system for structural health monitoring with guided ultrasonic waves and piezoelectric transducers. Struct. Infrastruct. Eng. 2013, 9, 1177–1186. [Google Scholar] [CrossRef]
- Raghavan, A.; Cesni, K.C.E.S. Review of guided-wavd structural health monitoring. Shock Vib. Dig. 2007, 39, 91–114. [Google Scholar] [CrossRef]
- Ciang, C.C.; Lee, J.R.; Bang, H.J. Structural health monitoring for a wind turbine system: A review of damage detection methods. Meas. Sci. Technol. 2008, 19, 122001. [Google Scholar] [CrossRef] [Green Version]
- Fan, W.; Qiao, P. Vibration-based Damage Identification Methods: A Review and Comparative Study. Struct. Health Monit. 2011, 10, 83–111. [Google Scholar] [CrossRef]
- Shokrani, Y.; Dertimanis, V.K.; Chatzi, E.N.; Savoia, M.N. On the use of mode shape curvatures for damage localization under varying environmental conditions. Struct. Control Health Monit. 2018, 25, e2132. [Google Scholar] [CrossRef]
- Vo-Duy, T.; Ho-Huu, V.; Dang-Trung, H.; Nguyen-Thoi, T. A two-step approach for damage detection in laminated composite structures using modal strain energy method and an improved differential evolution algorithm. Compos. Struct. 2016, 147, 42–53. [Google Scholar] [CrossRef]
- Sung, S.H.; Koo, K.Y.; Jung, H.J. Modal flexibility-based damage detection of cantilever beam-type structures using baseline modification. J. Sound Vib. 2014, 333, 4123–4138. [Google Scholar] [CrossRef]
- Taylor, S.G.; Zimmerman, D.C. Improved Experimental Ritz Vector Extraction with Application to Damage Detection. J. Vib. Acoust. 2010, 132, 011012. [Google Scholar] [CrossRef]
- Kordestani, H.; Xiang, Y.-Q.; Ye, X.-W.; Jia, Y.-K. Application of the Random Decrement Technique in Damage Detection under Moving Load. Appl. Sci. 2018, 8, 753. [Google Scholar] [CrossRef] [Green Version]
- Yi, T.-H.; Li, H.-N.; Zhao, X.-Y. Noise Smoothing for Structural Vibration Test Signals Using an Improved Wavelet Thresholding Technique. Sensors 2012, 12, 11205–11220. [Google Scholar] [CrossRef] [Green Version]
- Zhang, J.; Yan, R.; Gao, R.X.; Feng, Z. Performance enhancement of ensemble empirical mode decomposition. Mech. Syst. Signal Process. 2010, 24, 2104–2123. [Google Scholar] [CrossRef]
- Meng, F.; Mokrani, B.; Alaluf, D.; Yu, J.; Preumont, A. Damage Detection in Active Suspension Bridges: An Experimental Investigation. Sensors 2018, 18, 3002. [Google Scholar] [CrossRef] [Green Version]
- Bao, C.; Hao, H.; Li, Z.-X. Integrated ARMA model method for damage detection of subsea pipeline system. Eng. Struct. 2013, 48, 176–192. [Google Scholar] [CrossRef]
- Lakshmi, K.; Rao, A.R.M.; Gopalakrishnan, N. Singular spectrum analysis combined with ARMAX model for structural damage detection. Struct. Control. Health Monit. 2017, 24, e1960. [Google Scholar] [CrossRef]
- Mosavi, A.A.; Dickey, D.; Seracino, R.; Rizkalla, S. Identifying damage locations under ambient vibrations utilizing vector autoregressive models and Mahalanobis distances. Mech. Syst. Signal Process. 2012, 26, 254–267. [Google Scholar] [CrossRef]
- Fan, X.; Li, J.; Hao, H. Piezoelectric mpedance based damage detection in truss bridges based on time frequency ARMA model. Smart Struct. Syst. 2016, 18, 501–523. [Google Scholar] [CrossRef]
- Shadan, F.; Khoshnoudian, F.; Esfandiari, A. A frequency response-based structural damage identification using model updating method. Struct. Control. Health Monit. 2016, 23, 286–302. [Google Scholar] [CrossRef]
- Shi, J.-Y.; Spencer, B.F., Jr.; Chen, S.-S. Damage detection in shear buildings using different estimated curvature. Struct. Control Health Monit. 2018, 25, e2050. [Google Scholar] [CrossRef]
- Rose, J.L. A Baseline and Vision of Ultrasonic Guided Wave Inspection Potential. J. Press. Vessel Technol. 2002, 124, 273–282. [Google Scholar] [CrossRef]
- Monchalin, J.-P. Laser-ultrasonics: Principles and industrial applications. In Ultrasonic and Advanced Methods for Nondestructive Testing and Material Characterization; World Scientific: Hackensack, NJ, USA, 2007; pp. 79–115. [Google Scholar]
- Park, B.; Sohn, H.; Malinowski, P.; Ostachowicz, W. Delamination localization in wind turbine blades based on adaptive time-of-flight analysis of noncontact laser ultrasonic signals. Nondestruct. Test. Eval. 2017, 32, 1–20. [Google Scholar] [CrossRef]
- Chen, H.; Zuo, M.J.; Wang, X.; Hoseini, M.R. An adaptive Morlet wavelet filter for time-of-flight estimation in ultrasonic damage assessment. Measurement 2010, 43, 570–585. [Google Scholar] [CrossRef]
- Giurgiutiu, V. Lamb Wave Generation with Piezoelectric Wafer Active Sensors for Structural Health Monitoring; SPIE: Bellingham, DC, USA, 2003; Volume 5056. [Google Scholar]
- Mustapha, S.; Lu, Y.; Li, J.; Ye, L. Damage detection in rebar-reinforced concrete beams based on time reversal of guided waves. Struct. Health Monit. 2014, 13, 347–358. [Google Scholar] [CrossRef]
- Schaal, C.; Bischoff, S.; Gaul, L. Damage detection in multi-wire cables using guided ultrasonic waves. Struct. Health Monit. 2016, 15, 279–288. [Google Scholar] [CrossRef]
- Alleyne, D.N.; Cawley, P. The interaction of Lamb waves with defects. IEEE Trans. Ultrason. Ferroelectr. Freq. Control 1992, 39, 381–397. [Google Scholar] [CrossRef] [PubMed]
- Yeung, C.; Ng, C.T. Time-domain spectral finite element method for analysis of torsional guided waves scattering and mode conversion by cracks in pipes. Mech. Syst. Signal Process. 2019, 128, 305–317. [Google Scholar] [CrossRef]
- Wang, C.H.; Rose, J.T.; Chang, F.-K. A synthetic time-reversal imaging method for structural health monitoring. Smart Mater. Struct. 2004, 13, 415–423. [Google Scholar] [CrossRef]
- Zhao, X.; Gao, H.; Zhang, G.; Ayhan, B.; Yan, F.; Kwan, C.; Rose, J.L. Active health monitoring of an aircraft wing with embedded piezoelectric sensor/actuator network: I. Defect detection, localization and growth monitoring. Smart Mater. Struct. 2007, 16, 1208–1217. [Google Scholar] [CrossRef]
- Michaels, J.E.; Michaels, T.E. Guided wave signal processing and image fusion for in situ damage localization in plates. Wave Motion 2007, 44, 482–492. [Google Scholar] [CrossRef]
- Ng, C.T.; Veidt, M. A Lamb-wave-based technique for damage detection in composite laminates. Smart Mater. Struct. 2009, 18, 074006. [Google Scholar] [CrossRef]
- Soleimanpour, R.; Ng, C.-T. Locating delaminations in laminated composite beams using nonlinear guided waves. Eng. Struct. 2017, 131, 207–219. [Google Scholar] [CrossRef] [Green Version]
- Joglekar, D.M.; Mitra, M. Time domain analysis of nonlinear frequency mixing in a slender beam for localizing a breathing crack. Smart Mater. Struct. 2016, 26, 025009. [Google Scholar] [CrossRef]
- He, S.; Ng, C.T. Modelling and analysis of nonlinear guided waves interaction at a breathing crack using time-domain spectral finite element method. Smart Mater. Struct. 2017, 26, 085002. [Google Scholar] [CrossRef]
- Guan, R.; Lu, Y.; Wang, K.; Su, Z. Fatigue crack detection in pipes with multiple mode nonlinear guided waves. Struct. Health Monit. 2019, 18, 180–192. [Google Scholar] [CrossRef]
- Rose, L.R.F.; Wang, C.H. Mindlin plate theory for damage detection: Imaging of flexural inhomogeneities. J. Acoust. Soc. Am. 2010, 127, 754–763. [Google Scholar] [CrossRef]
- Ng, C.T. A two-stage approach for quantitative damage imaging in metallic plates using Lamb waves. Enarthquakes Struct. 2015, 8, 821–841. [Google Scholar] [CrossRef] [Green Version]
- Niethammer, M.; Jacobs, L.J.; Qu, J.; Jarzynski, J. Time-frequency representation of Lamb waves using the reassigned spectrogram. J. Acoust. Soc. Am. 2000, 107, L19–L24. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Bandara, S.; Rajeev, P.; Gad, E.; Sriskantharajah, B.; Flatley, I. Damage detection of in service timber poles using Hilbert-Huang transform. NDT E Int. 2019, 107, 102141. [Google Scholar] [CrossRef]
- Feng, B.; Ribeiro, A.L.; Ramos, H.G. A new method to detect delamination in composites using chirp-excited Lamb wave and wavelet analysis. NDT E Int. 2018, 100, 64–73. [Google Scholar] [CrossRef]
- Kim, C.-Y.; Park, K.-J. Mode separation and characterization of torsional guided wave signals reflected from defects using chirplet transform. NDT E Int. 2015, 74, 15–23. [Google Scholar] [CrossRef]
- Wandowski, T.; Malinowski, P.; Ostachowicz, W. Circular sensing networks for guided waves based structural health monitoring. Mech. Syst. Signal Process. 2016, 66, 248–267. [Google Scholar] [CrossRef]
- Wandowski, T.; Malinowski, P.; Ostachowicz, W.M. Damage detection with concentrated configurations of piezoelectric transducers. Smart Mater. Struct. 2011, 20, 025002. [Google Scholar] [CrossRef]
- Ma, L.; Du, M.; Wang, Z.; Chung, H.; Li, F.; Cheung, C.A.S. Damage Assessment of High-Speed EMU Train Bolsters via PZT Sensor Network-Based SHM Technology. In Proceedings of the 11th International Workshop on Structural Health Monitoring, Stanford, CA, USA, 12–14 September 2017. [Google Scholar] [CrossRef]
- Dai, H.; Gallo, G.J.; Schumacher, T.; Thostenson, E.T. A Novel Methodology for Spatial Damage Detection and Imaging Using a Distributed Carbon Nanotube-Based Composite Sensor Combined with Electrical Impedance Tomography. J. Nondestruct. Eval. 2016, 35, 26. [Google Scholar] [CrossRef]
- Lau, K.; Chan, C.; Zhou, L.; Jin, W. Strain monitoring in composite-strengthened concrete structures using optical fibre sensors. Compos. Part B Eng. 2001, 32, 33–45. [Google Scholar] [CrossRef]
- Hackmann, G.; Sun, F.; Castaneda, N.; Lu, C.; Dyke, S. A holistic approach to decentralized structural damage localization using wireless sensor networks. Comput. Commun. 2012, 36, 29–41. [Google Scholar] [CrossRef] [Green Version]
- Bhuiyan, M.Z.A.; Wang, G.; Cao, J.; Wu, J. Energy and bandwidth-efficient wireless sensor networks for monitoring high-frequency events. In Proceedings of the 2013 IEEE International Conference on Sensing, Communications and Networking (SECON), New Orleans, LA, USA, 24–27 June 2013; pp. 194–202. [Google Scholar]
- Liu, X.; Cao, J.; Lai, S.; Yang, C.; Wu, H.; Xu, Y.L. Energy efficient clustering for WSN-based structural health monitoring. In Proceedings of the 2011 Proceedings IEEE INFOCOM, Shanghai, China, 10–15 April 2011; pp. 2768–2776. [Google Scholar]
- Bhuiyan, M.Z.A.; Wang, G.; Cao, J. Sensor Placement with Multiple Objectives for Structural Health Monitoring in WSNs. In Proceedings of the 2012 IEEE 14th International Conference on High Performance Computing and Communication & 2012 IEEE 9th International Conference on Embedded Software and Systems, Liverpool, UK, 25–27 June 2012; pp. 699–706. [Google Scholar]
- Bhuiyan, M.Z.A.; Wang, G.; Cao, J.; Wu, J. Deploying wireless sensor networks with fault-tolerance for structural health monitoring. IEEE Trans. Comput. 2015, 64, 382–395. [Google Scholar] [CrossRef]
- Jang, S.; Jo, H.; Cho, S.; Mechitov, K.; Rice, J.A.; Sim, S.-H.; Jung, H.-J.; Yun, C.-B.; Spencer, B.F., Jr.; Agha, G. Structural health monitoring of a cable-stayed bridge using smart sensor technology: Deployment and evaluation. Smart Struct. Syst. 2010, 6, 439–459. [Google Scholar] [CrossRef] [Green Version]
- Spencer, B.; Cho, S. Wireless smart sensor technology for monitoring civil infrastructure: Technological developments and full-scale applications. In Proceedings of the World Congress on Advances in Structural Engineering and Mechanics (ASEM’11), Seoul, Korea, 18–23 September 2011; pp. 1–28. [Google Scholar]
- Kim, S.; Pakzad, S.; Culler, D.; Demmel, J.; Fenves, G.; Glaser, S.; Turon, M. Health monitoring of civil infrastructures using wireless sensor networks. In Proceedings of the 6th International Conference on Information Processing in Sensor Networks, Cambridge, MA, USA, 25–27 April 2007; pp. 254–263. [Google Scholar]
- Ali, S.H.; Zaid, M.; Abdullah, M.; Khan, T.M.R. SHM of Concrete Bridge Structures using Wireless Sensor Networks. In Proceedings of the Smart SysTech 2018; European Conference on Smart Objects, Systems and Technologies, Munich, Germany, 12–13 June 2018; pp. 1–6. [Google Scholar]
- Wolfs, P.J.; Bleakley, S.; Senini, S.T.; Thomas, P. An autonomous, low cost, distributed method for observing vehicle track interactions. In Proceedings of the ASME/IEEE 2006 Joint Rail Conference, Atlanta, GA, USA, 4–6 April 2006; pp. 279–286. [Google Scholar]
- Tam, H.; Lee, T.; Ho, S.; Haber, T.; Graver, T.; Méndez, A.; House, K.; Tin, S. Utilization of fiber optic Bragg Grating sensing systems for health monitoring in railway applications. Struct. Health Monit. Quantif. Valid. Implement. 2007, 1, 2. [Google Scholar]
- Reason, J.M.; Chen, H.; Crepaldi, R.; Duri, S. Intelligent telemetry for freight trains. In Proceedings of the International Conference on Mobile Computing, Applications, and Services, San Diego, CA, USA, 26–29 October 2009; pp. 72–91. [Google Scholar]
- Westeon, P.; Ling, C.; Roberts, C.; Goodman, C.; Li, P.; Goodall, R. Monitoring vertical track irregularity from in-service railway vehicles. Proc. Inst. Mech. Eng. Part F J. Rail Rapid Transit 2007, 221, 75–88. [Google Scholar] [CrossRef]
- Weston, P.; Roberts, C.; Goodman, C.; Ling, C. Condition monitoring of railway track using in-service trains. In Proceedings of the 2006 IET International Conference on Railway Condition Monitoring, Birmingham, UK, 29–30 November 2006; pp. 26–31. [Google Scholar]
- Mori, H.; Tsunashima, H.; Kojima, T.; Matsumoto, A.; Mizuma, T. Condition monitoring of railway track using in-service vehicle. J. Mech. Syst. Transp. Logist. 2010, 3, 154–165. [Google Scholar] [CrossRef] [Green Version]
- Nuffer, J.; Bein, T. Application of piezoelectric materials in transportation industry. In Proceedings of the Global Symposium on Innovative Solutions for the Advancement of the Transport Industry, Donostia-San Sebastian, Spain, 4–6 October 2006. [Google Scholar]
- Matsumoto, A.; Sato, Y.; Ohno, H.; Shimizu, M.; Kurihara, J.; Tomeoka, M.; Saitou, T.; Michitsuji, Y.; Tanimoto, M.; Sato, Y.; et al. Continuous observation of wheel/rail contact forces in curved track and theoretical considerations. Veh. Syst. Dyn. 2012, 50, 349–364. [Google Scholar] [CrossRef] [Green Version]
- Notay, J.K.; Safdar, G.A. A wireless sensor network based structural health monitoring system for an airplane. In Proceedings of the 17th International Conference on Automation and Computing, Huddersfield, UK, 10 September 2011; pp. 240–245. [Google Scholar]
- Becker, T.; Kluge, M.; Schalk, J.; Tiplady, K.; Paget, C.; Hilleringmann, U.; Otterpohl, T. Autonomous sensor nodes for aircraft structural health monitoring. IEEE Sens. J. 2009, 9, 1589–1595. [Google Scholar] [CrossRef]
- Arms, S.; Galbreath, J.; Townsend, C.; Churchill, D.; Corneau, B.; Ketcham, R.; Phan, N. Energy harvesting wireless sensors and networked timing synchronization for aircraft structural health monitoring. In Proceedings of the 2009 1st International Conference on Wireless Communication, Vehicular Technology, Information Theory and Aerospace & Electronic Systems Technology, Aalborg, Denmark, 17–20 May 2009; pp. 16–20. [Google Scholar]
- Cao, J.; Liu, X. Structural Health Monitoring Using Wireless Sensor Networks. In Mobile and Pervasive Computing in Construction; John Wiley & Sons Ltd.: Hoboken, NJ, USA, 2012; pp. 210–236. [Google Scholar] [CrossRef]
- Dos Santos, I.L.; Pirmez, L.; Lemos, É.T.; Delicato, F.C.; Pinto, L.A.V.; de Souza, J.N.; Zomaya, A.Y. A localized algorithm for Structural Health Monitoring using wireless sensor networks. Inf. Fusion 2014, 15, 114–129. [Google Scholar] [CrossRef]
- Beygzadeh, S.; Salajegheh, E.; Torkzadeh, P.; Salajegheh, J.; Naseralavi, S. Optimal sensor placement for damage detection based on a new geometrical viewpoint. Int. J. Optim. Civ. Eng. 2013, 3, 1–21. [Google Scholar]
- Hodge, V.J.; O’Keefe, S.; Weeks, M.; Moulds, A. Wireless sensor networks for condition monitoring in the railway industry: A survey. IEEE Trans. Intell. Transp. Syst. 2015, 16, 1088–1106. [Google Scholar] [CrossRef]
- Chintalapudi, K.; Fu, T.; Paek, J.; Kothari, N.; Rangwala, S.; Caffrey, J.; Govindan, R.; Johnson, E.; Masri, S. Monitoring civil structures with a wireless sensor network. IEEE Internet Comput. 2006, 10, 26–34. [Google Scholar] [CrossRef]
- Cammarano, A.; Spenza, D.; Petrioli, C. Energy-harvesting WSNs for structural health monitoring of underground train tunnels. In Proceedings of the 2013 IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS), Turin, Italy, 14–19 April 2013; pp. 75–76. [Google Scholar]
- Dilhac, J.-M.; Bafleur, M. Energy harvesting in aeronautics for battery-free wireless sensor networks. IEEE Aerosp. Electron. Syst. Mag. 2014, 29, 18–22. [Google Scholar] [CrossRef] [Green Version]
- Qiu, L.; Yuan, S.; Liu, P.; Qian, W. Design of an all-digital impact monitoring system for large-scale composite structures. IEEE Trans. Instrum. Meas. 2013, 62, 1990–2002. [Google Scholar] [CrossRef]
- Li, B.; Wang, D.; Wang, F.; Ni, Y.Q. High quality sensor placement for SHM systems: Refocusing on application demands. In Proceedings of the 2010 Proceedings IEEE INFOCOM, San Diego, CA, USA, 14–19 March 2010; pp. 1–9. [Google Scholar]
- Low, K.S.; Win, W.N.N.; Er, M.J. Wireless sensor networks for industrial environments. In Proceedings of the International Conference on Computational Intelligence for Modelling, Control and Automation and International Conference on Intelligent Agents, Web Technologies and Internet Commerce (CIMCA-IAWTIC’06), Vienna, Austria, 28–30 November 2005; pp. 271–276. [Google Scholar]
- Werb, J.; Newman, M.; Berry, V.; Lamb, S.; Sexton, D.; Lapinski, M. Improved quality of service in IEEE 802.15.4 mesh networks. In Proceedings of the International Workshop on Wireless and Industrial Automation, San Francisco, CA, USA, 7 March 2005; pp. 1–6. [Google Scholar]
- Bannister, K.; Giorgetti, G.; Gupta, S. Wireless sensor networking for hot applications: Effects of temperature on signal strength, data collection and localization. In Proceedings of the 5th Workshop on Embedded Networked Sensors (HotEmNets’08), Charlottesville, VA, USA, 2–3 June 2008. [Google Scholar]
IT | Ashtead Technology, FLIR Systems Ltd., NDT Global Services Ltd., Oceanscan |
EM | Ashtead Technology, Doosan Babcock, GE M & C, MISTRAS Group Ltd., NDT Consultants Ltd., Olympus |
ECT | Baugh & Weedon, Bowyer Engineering Ltd., Doosan Babcock, Eddyfi, ETher NDE, Fidgeon Ltd., GB Inspection Systems Ltd., GE M&C |
UF | Advanced OEM Solutions, Ashtead Technology, Baugh & Weedon, Bowyer Engineering Ltd., GE M&C Inspection Technologies, Labquip NDT Ltd., Oceanscan, Olympus, M2M (Eddyfi), Sonatest Ltd. |
Technology | Maximum Coverage Range | Power Consumption | Data Transmission Rate | Frequency |
---|---|---|---|---|
IEEE 802.11 | 150 m | High | 54 Mbps | 2.4 GHz |
ZigBee | 300 m | Low | Max. 250 Kbps | 868 MHz/902–928 MHz/2.4 GHz |
ISA100.11a | 150 m | Low | Max. 250 Kbps | 2.4 GHz |
Bluetooth | 300 m | Medium | Max. 2 Mbps | 2.4 GHz |
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. |
© 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Mustapha, S.; Lu, Y.; Ng, C.-T.; Malinowski, P. Sensor Networks for Structures Health Monitoring: Placement, Implementations, and Challenges—A Review. Vibration 2021, 4, 551-585. https://doi.org/10.3390/vibration4030033
Mustapha S, Lu Y, Ng C-T, Malinowski P. Sensor Networks for Structures Health Monitoring: Placement, Implementations, and Challenges—A Review. Vibration. 2021; 4(3):551-585. https://doi.org/10.3390/vibration4030033
Chicago/Turabian StyleMustapha, Samir, Ye Lu, Ching-Tai Ng, and Pawel Malinowski. 2021. "Sensor Networks for Structures Health Monitoring: Placement, Implementations, and Challenges—A Review" Vibration 4, no. 3: 551-585. https://doi.org/10.3390/vibration4030033