Faults detection and failures prediction using vibration analysis
Author:
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/7347833/7356451/07356493.pdf?arnumber=7356493
Cited by 10 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献
1. Spring-like Triboelectric Nanogenerator for Monitoring Body Vibration State of the Ship Power Equipment;Journal of Marine Science and Engineering;2023-11-06
2. Electrical Motor Fault Detection System using AI's Random Forest Classifier Technique;2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET);2023-04-29
3. LOPdM: A Low-Power On-Device Predictive Maintenance System Based on Self-Powered Sensing and TinyML;IEEE Transactions on Instrumentation and Measurement;2023
4. Accompanying deep framework for faults in motor and gearbox with disproportion vibrational samples;Neural Computing and Applications;2022-12-13
5. Machine condition monitoring enabled by broad range vibration frequency detecting triboelectric nano-generator (TENG)-based vibration sensors;Nano Energy;2022-07
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