振动
稳健性(进化)
人工智能
可视化
计算机科学
计算机视觉
模式识别(心理学)
降噪
卡尔曼滤波器
球(数学)
故障检测与隔离
控制理论(社会学)
声学
数学
生物化学
化学
物理
控制(管理)
执行机构
基因
数学分析
作者
Bin Zhu,Zhengyu Yang,Yupeng Jia,Shengxin Chen,Jiahui Song,sanman Liu,Ping Li,Feng Li,Dengao Li
出处
期刊:ACM transactions on the internet of things
[Association for Computing Machinery]
日期:2023-11-30
卷期号:4 (4): 1-26
被引量:1
摘要
Vibration is a normal reaction that occurs during the operation of machinery and is very common in industrial systems. How to turn fine-grained vibration perception into visualization, and further predict mechanical failures and reduce property losses based on visual vibration information, which has aroused our thinking. In this article, the phase information generated by the tag is processed and analyzed, and MFD is proposed, a real-time vibration monitoring and fault-sensing discrimination system. MFD extracts phase information from the original RF signal and converts it into a Markov transition map by introducing White Gaussian Noise and a low-pass filter for denoising. To accurately predict the failure of machinery, a deep and machine learning model is introduced to calculate the accuracy of failure analysis, realizing real-time monitoring and fault judgment. The test results show that the average recognition accuracy of vibration can reach 96.07%, and the average recognition accuracy of forward rotation, reverse rotation, oil spill, and screw loosening of motor equipment during long-term operation can reach 98.53%, 99.44%, 97.87%, and 99.91%, respectively, with high robustness.
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