Self‐Powered Bearing Sensing and Real‐Time Fault Diagnosis Enabled by Non‐Invasive Triboelectric Sensors and Edge AI Acceleration
compared with CPU and GPU implementations, respectively, with a latency as low as 0.108ms and a fault-classification accuracy of 98%. This study achieves the integration of self-powered sensors with edge AI acceleration, providing a new pathway for real-time diagnostics of smart bearings.
Authors
- J C Zhang
- Meng Li (ORCID: https://orcid.org/0000-0001-9132-3208)
- Yanxue Wang (ORCID: https://orcid.org/0000-0001-8739-4740)
- Z.-Y. Liu
- Lihua Han
- Kehui Zhu (ORCID: https://orcid.org/0009-0002-7984-6684)
- Yiming Guo
- Xinming Li
Institutions
- Beijing University of Civil Engineering and Architecture (CN)
Publication Details
- Journal
- Advanced Science
- Published
- 2026-04-22
- DOI
- https://doi.org/10.1002/advs.75373
- Primary Topic
- Advanced Sensor and Energy Harvesting Materials
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Natural Science Foundation of China