基于数学运算的纸机轴承检测方法设计
Design of Paper Machine Bearing Detection Method Based on Mathematical Operation
成佳 1王莹1
作者信息
- 1. 铜川职业技术学院,陕西 铜川,727031
- 折叠
摘要
为实现针对造纸机轴承的自动化检测,维持造纸工艺流程的稳定运行,基于纸机轴承接触角、流动体数量以及滚动体直径等参数建立轴承故障特征频率分析模型,并采用二次迭代的方法对常规模态分解算法进行优化,进而对轴承故障特征频率分析模型进行求解,最终输出轴承故障的Hilbert谱图.为验证模型与算法的可行性,采用加速度传感器采集轴承的振动信号并对信号数据加以分析.试验结果显示,二次迭代模态分解算法能够有效识别出纸张轴承在运行过程中的频率异常状态,识别精度显著优于常规模态分解算法,有助于提高造纸企业对纸机轴承故障检测的准确率.
Abstract
In order to realize the automatic detection of paper machine bearings and maintain the stable operation of the papermaking process,the bearing fault eigenfrequency analysis model was established based on the parameters such as the contact length,the number of flowing elements and the diameter of the rolling elements of the paper machine bearing,and the constant-scale state decomposition algorithm was optimized by the second iteration method,and then the bearing fault eigenfrequency analysis model was solved,and the Hilbert spectrum of bearing failure was finally output.In order to verify the feasibility of the model and algorithm,an accelerometer was used to collect the vibration signal of the bearing and analyze the signal data.The test results show that the second-iteration modal decomposition algorithm can effectively identify the frequency anomalies of paper bearings during operation,and the recognition accuracy is significantly better than that of the normal-scale morphological decomposition algorithm,which is helpful to improve the accuracy of paper machine bearing fault detection in papermaking enterprises.
关键词
纸机轴承/故障特征频率/模态分解/仿真分析Key words
paper machine bearing/fault eigenfrequency/modal decomposition/simulation analysis引用本文复制引用
出版年
2024
NETL
NSTL
万方数据