Fully Flexible Integrated and Self-Powered Smart Bending Strain Skin for Wireless Monitoring of Heavy Industrial Equipment
The skins used for structural health monitoring (SHM) are crucial intelligent equipment for industrial applications and have garnered extensive attention in recent years. Here, we present a simple-to-fabricate, flexible, self-powered, fully integrated smart bending strain skin (BSS) that combines a strain sensor array with flexible circuits on a polyimide (PI) substrate. The strain sensor array is manufactured by screen-printing stretchable conductive silver pastes onto precisely designed 2-D surface structures. The integration of the microcrack structure on the surface with the lamellar structure of the stretchable conductive silver paste ensured the linearity and enhanced sensitivity of the strain sensor, with strain coefficients of 35 for both tensile and compressive strains and the correlation coefficient <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${R} ^{2}$ </tex-math></inline-formula> equal to 0.99. The flexible circuit board integrates the main control chip, a wireless transmission module, and a flexible solar panel that powers the entire system continuously under standard illumination. This setup enables strain monitoring of various industrial equipments and clear display on a host computer. Additionally, a wind-level recognition model based on SHM of fan blades in wind turbines has been developed, achieving a detection rate of 92% across five Beaufort wind levels. All these studies indicate that this integrated skinning system has significant potential for improving manufacturing efficiency and monitoring across various heavy industrial equipment applications.
Authors
- Chen Wei (ORCID: https://orcid.org/0009-0002-0415-8633)
- Yangyanhao Guo (ORCID: https://orcid.org/0009-0002-9020-1553)
- Kun Zhu (ORCID: https://orcid.org/0000-0001-6820-8981)
- Xiujian Chou (ORCID: https://orcid.org/0000-0001-5677-0934)
- Zhiqiang Lan (ORCID: https://orcid.org/0000-0003-3288-8879)
- Jian He (ORCID: https://orcid.org/0000-0002-6353-4697)
- Xiaojuan Hou (ORCID: https://orcid.org/0000-0002-5964-3719)
- Song Xiao-guang (ORCID: https://orcid.org/0000-0002-9572-599X)
- Ruoyang Zhang (ORCID: https://orcid.org/0009-0004-9889-3536)
- Kunru Li
Institutions
- North University of China (CN)
- Soochow University (CN)
Publication Details
- Journal
- IEEE Transactions on Instrumentation and Measurement
- Published
- 2024-12-04
- DOI
- https://doi.org/10.1109/tim.2024.3502818
- Citations
- 2
- Primary Topic
- Industrial Vision Systems and Defect Detection
- Type
- article
- Field-Weighted Citation Impact
- 0.70
Funders
- National Natural Science Foundation of China
- Natural Science Foundation of Shanxi Province