Multi‐Phase Rotating Disk Triboelectric Nanogenerator with DC Output for Speed Monitoring

Abstract

Triboelectric nanogenerators (TENGs) are highly efficient devices for harvesting mechanical energy. Nevertheless, conventional TENGs often produce AC output, which, coupled with their high crest factor and pulsed output characteristics, poses limitations on their widespread adoption in real scenarios. In this paper, a multi‐phase rotating disk triboelectric nanogenerator (MPRD‐TENG) characterized by a low crest factor and DC output is prepared through the method of phase superposition. The findings reveal that by enhancing these parameters, namely, increasing the number of rotating disk TENGs, augmenting the number of grids, and elevating the rotational speed, the crest factor of the MPRD‐TENG can be effectively reduced. Furthermore, this innovative MPRD‐TENG demonstrates its versatility by successfully powering a fire alarm system, thereby offering a promising solution for early warning and monitoring of offshore oil exploration fires. Ultimately, the implementation of machine learning algorithms to train the DC output data collected by the MPRD‐TENG significantly enhances the capability to predict and classify signals corresponding to varying speeds with greater precision. Consequently, the integration of machine learning methods not only facilitates a more effective warning system but also bolsters monitoring capabilities for unforeseen situations encountered in real‐world engineering projects.