Automatic Damage Identification Using Wireless Sensors, Built on a Cheap Element Base

Authors

  • A. V. Basko Prydniprovska State Academy of Civil Engineering and Architecture, Dnipro
  • O. A. Ponomarova Prydniprovska State Academy of Civil Engineering and Architecture, Dnipro

DOI:

https://doi.org/10.31649/1997-9266-2023-169-4-6-15

Keywords:

real time dynamic systems, cheap wireless sensor, accelerometer, monitoring system, damage identification

Abstract

The importance of research aimed at structural monitoring of architectural constructions is determined by the density of buildings, aging and the influence of aggressive operating conditions of the environment . This work is aimed at developing of a sensor assembly, on the one hand, economically cheap and appropriate for use in monitoring systems. On the other hand, the sensor node should not be inferior to existing solutions in terms of technical characteristics and computing capabilities. First of all, the existing microprocessor and microcontroller sensor nodes were analyzed in order to select the most used architectural solutions for wireless sensor nodes. Thus, the ST Microelectronics STM32WB55CG microcontroller with a built-in wireless communication core was chosen for the sensor node prototype for the first time. A combination of three accelerometers ST Microelectronics LIS3DSH was used in one sensor node in order to increase the fault tolerance of the prototype. A distinctive feature of this work is the search and application of effective algorithms for the identification and monitoring of the state of the structure for inexpensive sensor nodes. The study proves that the use of neural network algorithms requires the presence of a large database in an intact state for training, and the time spent on both training and identification requires significant computing power from the microcontroller, which makes such algorithms unsuitable for the use in dynamic systems of real time. Therefore, a prototype of a wireless sensor was assembled, which was accordingly tested on an architectural structure near the railway to check the sensitivity of the sensor node. The research also provides comparison results of two statistical damage identification algorithms, such as Euclidean norm and Mahalanobis distance.

Author Biographies

A. V. Basko, Prydniprovska State Academy of Civil Engineering and Architecture, Dnipro

Post-Graduate Student of the Chair of Information Technology and Applied Mathematics

O. A. Ponomarova, Prydniprovska State Academy of Civil Engineering and Architecture, Dnipro

Cand. Sc. (Eng.), Associate Professor, Head of the Chair of Information Technology and Applied Mathematics

References

E. Toledo Junior, A. Cury, and J. Landre Junior, “Assessment of low-cost wireless sensors for structural health monitoring applications,” Revista IBRACON de Estruturas e Materiais, vol. 14, no. 2, pp. 1-14, 2021. https://doi.org/10.1590/s1983-41952021000200013 .

M. Z. Sarwar, M. R. Saleem, J. W. Park, D. S. Moon, and D. J. Kim, “Multimetric Event-Driven System for Long-Term Wireless Sensor Operation for SHM Applications,” IEEE Sensors Journal, vol. 20, no. 10, pp. 5350-5359, 2020. https://doi.org/10.1109/jsen.2020.2970710 .

B. Spencer, J. W. Park, K. Mechitov, H. Jo, and G. Agha, “Next Generation Wireless Smart Sensors Toward Sustainable Civil Infrastructure,” Procedia Engineering, vol. 171, pp. 5–13, 2017. https://doi.org/10.1016/j.proeng.2017.01.304.

M. Varanis, A. L. Silva, P. H. A. Brunetto, and R. F. Gregolin, “Instrumentation for mechanical vibrations analysis in the time domain and frequency domain using the Arduino platform,” Revista Brasileira de Ensino de Fisica, vol. 38, no. 1, pp. 1-10, 2016. https://doi.org/10.1590/s1806-11173812063 .

A. Araujo, et al, “Wireless Measurement System for Structural Health Monitoring With High Time-Synchronization Accuracy,” IEEE Transactions on Instrumentation and Measurement, vol. 63, no. 3, pp. 801-810, 2012. https://doi.org/10.1109/tim.2011.2170889 .

S. Pandey, M. Haider, and N. Uddin, “Design and implementation of a low-cost wireless platform for remote bridge health monitoring,” International Journal of Emerging Technology and Advanced Engineering, vol. 6, no. 6, pp. 57-62, 2016.

A. A. Sana, A. S. Rasi, D. P. Pa, G. R. Veya, and M. D. Gesan, “Wireless Sensor Network Based Crack Detection on Concrete Bridges/Buildings,” International Journal of Engineering Trends and Technology, vol. 57, no. 2, pp. 54-58, 2018. https://doi.org/10.14445/22315381/ijett-v57p211 .

A. Entezami, H. Sarmadi, and S. Mariani, “An Unsupervised Learning Approach for Early Damage Detection by Time Series Analysis and Deep Neural Network to Deal with Output-Only (Big) Data,” Proceedings of 7th International Electronic Conference on Sensors and Applications, 2020. https://doi.org/10.3390/ecsa-7-08281 .

J. Morales-Valdez, M. A. Lopez, and W. Yu, “Damage detection of building structure based on vibration data and hysteretic model,” IEEE 15th International Conference on Automation Science and Engineering, 2019. https://doi.org/10.1109/coase.2019.8842996 .

R. P. Finotti, A. A. Cury, F. D. S. Barbosa, “An SHM approach using machine learning and statistical indicators extracted from raw dynamic measurements,” Latin American Journal of Solids and Structures, vol. 16, no. 2, pp. 1-17, 2019. https://doi.org/10.1590/1679-78254942.

S. Prabhu, and S. Atamturktur, “Feature Assimilation for Vibration Based Damage Detection,” Journal of Testing and Evaluation, vol. 41, no. 1, pp. 1-11, 2012. https://doi.org/10.1520/jte20120170 .

M. Gordan, H. A. Razak, Z. Ismail, and K. Ghaedi, “Recent Developments in Damage Identification of Structures Using Data Mining,” Latin American Journal of Solids and Structures, vol. 14, no. 13, pp. 2373-2401, 2017. https://doi.org/10.1590/1679-78254378 .

J. A. Rice, et al, “Flexible smart sensor framework for autonomous structural health monitoring,” Smart Structures and Systems, vol. 6, no. 5_6, pp. 423-438, 2010. https://doi.org/10.12989/sss.2010.6.5_6.423 .

E. Sazonov, Li. Haodong, D. Curry, and P. Pillay, “Self-Powered Sensors for Monitoring of Highway Bridges,” IEEE Sensors Journal, vol. 9, no. 11, pp. 1422-1429, 2009. https://doi.org/10.1109/jsen.2009.2019333 .

L. R. Ticona Melo, D. Ribeiro, R. Calcada, and T. N. Bittencourt, “Validation of a vertical train–track–bridge dynamic interaction model based on limited experimental data,” Structure and Infrastructure Engineering, vol. 16, no. 1, pp. 181-201, 2019. https://doi.org/10.1080/15732479.2019.1605394 .

J. Pacheco, G. Oliveira, F. Magalhaes, C. Moutinho, and L. Cunha, “Evaluation of low cost vibration based damage detection systems,” Journal of Physics: Conference Series, vol. 1037, no. 052005, pp. 1-8, 2018. https://doi.org/10.1088/1742-6596/1037/5/052005 .

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Published

2023-08-31

How to Cite

[1]
A. V. . Basko and O. A. . Ponomarova, “Automatic Damage Identification Using Wireless Sensors, Built on a Cheap Element Base”, Вісник ВПІ, no. 4, pp. 6–15, Aug. 2023.

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Section

Automation and information-measuring equipment

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