Revolutionary Self-Powered Vibration Sensor with AI Integration for Machine Fault Diagnosis
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- 2024-02-29
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- Developed a self-powered vibration sensor based on triboelectric nano-generator using highly elastic nano-composite materials and 3D printing technology -
- Integrated AI technology for machinery identification, fault monitoring, failure diagnosis and prediction -
- Published in Nano Energy, a world-renowned energy journal (IF: 17.6) -
The research team of Professor Jae-young Park (Department of Electronic Engineering) designed and fabricated a triboelectric nano-generator based on a pillar array structure with high elasticity using 3D printing technology and a siloxene-ecoflex nano composite, and succeeded in developing a highly sensitive self-powered vibration sensor that can diagnose defects and faults by monitoring broadband aperiodic machine vibrations in real time. They also demonstrated that it is possible to identify patterns, defects, and potential problems in machinery and prevent machine failures by analyzing mechanical vibration data in detail by applying artificial intelligence (AI) technology.
<Professor Jae-young Park (left) and Dr. Trilochan (right)>
Unlike traditional triboelectric nanogenerators which rely on contact and separation, the vibration sensor developed by the team is designed as an elastic macro-column and utilizes the flexibility and inherent elasticity of the pillar instead of relying on direct contact to detect vibrations. Through this innovative approach, the sensor responds to random electrodynamic motions of macro-pillars across various frequencies and amplitudes to precisely detect broadband aperiodic machine vibrations.
The research was supported by the National Research Foundation of Korea (NRF-2020R1A2C2012820) and the Industrial Technology Innovation Program by the Ministry of Trade, Industry, and Energy (RS-2022-00154983, Development of a self-sustaining power sensor platform for low-power sensors and actuators). The research findings were published in Nano Energy by Elsevier Publishing, a leading journal specializing in energy materials and device technology, with an Impact Factor of 17.6.
Web link: (https://doi.org/10.1016/j.nanoen.2023.108929)
<Conceptual diagram and performance of the elastic pillar array-based triboelectric nanogenerator and self-powered vibration sensor for real-time monitoring and diagnosis of machine conditions>