摘要
为了提高滤波器下故障信号时域波形有效信息的提取能力,设计一种开关卡尔曼滤波算法应用到叶轮故障振动信号特征提取领域。预测出所有时间点监测数据最可能呈现出的状态,将噪声除去且有效辨别各冲击成分,进一步加强信号的信噪比。仿真信号结果表明,滤波后信噪比接近于噪声添加后的信噪比,脉冲辨别成效显著。试验验证结果表明,测量得到信号中有着显著的噪声,各个时刻评判出的信号成分都与现实情况相符。该研究可以拓宽到其他的机械传动领域,且具有很好的市场应用价值。
In order to improve the effective information extraction ability of fault signal time-domain waveform under filter,a switched Kalman filter algorithm was designed and applied to the feature extraction field of impeller fault vibration signal.The most likely state of monitoring data at all time points was predicted,the noise was removed and each impact component was effectively distinguished,and the signal to noise ratio was further strengthened.The simulation signal results show that the signal to noise ratio after filtering should be close to the noise ratio after adding noise,and the pulse discrimination effect is remarkable.The experimental verification results indicate that there is a significant noise in the measured signal,and the components of the signal judged at every moment are consistent with the reality.This research can be extended to other fields of mechanical transmission and has good market application value.
作者
袁艳
李峰
王东
YUAN Yan;LI Feng;WANG Dong(School of Mechanical and Electrical Engineering,Xi'an Traffic Engineering Institute,Xi'an 710399,China;School of Mechanical Engineering,Xi'an Jiaotong University,Xi'an 710049,China;Xi'an Research Institute,China Coal Science and Industry Group,Xi'an 710076,China)
出处
《机械制造与自动化》
2024年第2期67-70,共4页
Machine Building & Automation
基金
西安交通工程学院中青年基金项目(2022KY-09)。
关键词
叶轮
开关卡尔曼滤波
特征提取
噪声信号
rolling bearing
switched Kalman filter
feature extraction
noise sign
