Hybrid Generator for Efficient Vibration Energy Harvesting and Self-Powered Gear Condition Monitoring Applications in Industrial Equipment
The hybrid energy harvester installed on mechanical equipment can convert vibration energy into electrical energy to power sensors, microcontrollers, wireless communication, etc., effectively avoiding cumbersome battery maintenance and line failures. With the high sensitivity to vibration changes, the hybrid energy harvester can also be used to monitor the state of the mechanisms while collecting vibration energy, avoiding accidents and economic losses caused by malfunction. In this work, we designed a highly integrated device that combines energy harvesting and sensing detection functions. The main structure uses magnetically permeable materials to form a unique electromagnetic generator (EMG) with magnetic circuit switching. Piezoelectric generator (PEG) and triboelectric nanogenerator (TENG) are distributed on both sides of the cantilever beam, and the three structures are integrated together through the cantilever beam, which not only makes full use of the structural space of the device but also improves the power generation efficiency. Under the excitation of a vibration frequency of 8 Hz and an acceleration of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$2{g}$ </tex-math></inline-formula>, the peak power of the EMG, PEG, and TENG modules were 22 mW, 1.9 mW, and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$48~\mu $ </tex-math></inline-formula>W, respectively. TENG unit was selected for identifying the gear state, achieving an accuracy rate of 93% in tests. Ultimately, the hybrid generator was applied to a car engine, successfully powering a thermometer or sensing detection systems, realizing timely signal transmission. The performance testing of the device demonstrates a broad application prospect in transportation and industrial production.
Authors
- Chunhui Du (ORCID: https://orcid.org/0000-0001-8063-7711)
- Xin Li (ORCID: https://orcid.org/0000-0001-7484-7931)
- Chenyang Xue (ORCID: https://orcid.org/0000-0003-0770-3975)
- Xiang Gao (ORCID: https://orcid.org/0009-0004-9441-2336)
- Juan Cui (ORCID: https://orcid.org/0000-0002-4025-4641)
- Yongqiu Zheng (ORCID: https://orcid.org/0000-0001-6644-5879)
- Kailun Bai
Institutions
- North University of China (CN)
Publication Details
- Journal
- IEEE Sensors Journal
- Published
- 2024-09-19
- DOI
- https://doi.org/10.1109/jsen.2024.3460379
- Primary Topic
- Machine Fault Diagnosis Techniques
- Type
- article
- Field-Weighted Citation Impact
- 0.00