Feature‐Reinforced Strategy for Enhancing the Accuracy of Triboelectric Vibration Sensing Toward Mechanical Equipment Monitoring

With the advancement of intelligent and refined manufacturing, the demand for vibration sensors in smart equipment has surged. Traditional commercial vibration sensors and triboelectric nanogenerator (TENG)-based sensors are limited to basic amplitude and frequency recognition, failing to address both self-powering and diagnostic needs due to inherent design constraints. To overcome these limitations, this study introduces a novel mechanism combining interface dipole energy and vacuum level optimization in triboelectric materials to explain charge generation and separation under vibration. A TENG device with polydimethylsiloxane (PDMS)-encapsulated metal electrode is designed and developed, enabling the precise recognition of equipment operating status through vibration waveform analysis. By optimizing interface contact area and electron transfer capacity, the device achieves enhanced signal clarity and the introduction of subtler characteristics in the signal waveform. Furthermore, the integration of a deep learning algorithm enables high-resolution classification of vibration states with an accuracy of 98.3% approximately, achieving effective monitoring of the operating status of the jaw crusher and vibrating screen. This work not only verifies the feasibility of designing a self-powered vibration sensor but also demonstrates its potential for real-time monitoring and diagnostic applications in smart equipment.