The wind-induced vibration of a remote sensing tower is the key factor affecting the stability of image sensing and structural reliability. Monitoring the vibration of a long-time unattended tower is critical to its proper operation. Currently, most monitoring devices are supplied with wired power or battery, significantly limiting their practical applications in remote areas. In this paper, a self-powered vibration sensing device based on hybrid electromechanical conversion mechanisms is proposed. The device depends on a cylindrical magnetic levitation structure sensitive to ambient vibration for transferring mechanical energy and takes as a dual-functional heterogeneous integrated system comprising electromagnetic, piezoelectric, and triboelectric generators. When the device vibrates under environmental force driving, the suspension magnet reciprocates vertically and generates induced electromagnetic energy, which is used to power the device. Moreover, the triboelectric and piezoelectric voltages, respectively originating from magnet impact on two separation friction materials and magnetic field repulsion-induced strain deformation of a piezoelectric sheet, are used as the synergistic sensing signals. To improve the output energy, a set of dual-segmented annular coils is designed in an electromagnetic generator, which greatly avoids the obstructive effect of the suspended magnet on the magnetic flux change at its end. Compared with a whole isochoric coil, it increases the output voltage by 78.3%. For the triboelectric sensing module, a silicone film with a large specific surface area is fabricated via 3D modification, which improves the output voltage by 29.4%. Furthermore, a pair of piezoelectric sensing modules is set to improve the accuracy of comparative sensing data. The experimental measurement shows that the device maintains a high sensitivity of 6.711 V (m s?2)?1 and excellent linearity of 0.991 in the range of 0–14 m s?2. This work provides a practical strategy for the vibration monitoring of remote sensing tower and exhibits attractive potential in early warning and data analysis.
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