机械设备振动信号采集对故障优化检测仿真

发布时间：2018-06-20 13:28

本文选题：机械设备 + 振动信号　；参考：《计算机仿真》2017年06期

【摘要】：对机械设备的振动信号进行采集在检测设备故障方面具有重要意义。由于机械设备使用环境的不同,使得机械设备振动信号存在不定性。传统的故障检测方法主要对机械设备使用环境进行了分析,忽略了机械设备振动信号采集对故障检测结果的影响,导致检测结果不准确、效率低的问题。提出基于小波域维纳滤波的机械设备故障检测方法。先将维纳滤波和小波阕值滤波相结合,组建机械设备振动信号采集模型,得出真实信号与滤波后信号误差,融合于小波阈值滤波原理对含噪声信号进行正交小波变换,并通过快速独立分量分析原理对机械设备振动信号进行分离,组建具有单位方差的信号初始分离矩阵,并求出源信号的估计值,依据估计值完成对机械设备振动信号的采集,实现对机械故障设备故障的检测优化。仿真结果证明,采用所提方法进行信号采集,可提高机械设备故障检测的效率、减小检测误差、鲁棒性较强。
[Abstract]:It is very important to collect the vibration signal of mechanical equipment in detecting the fault of the equipment. The vibration signal of mechanical equipment is uncertain because of the difference of the using environment of mechanical equipment. The traditional fault detection method mainly analyzes the environment of mechanical equipment, neglecting the influence of mechanical equipment vibration signal collection on the result of fault detection, which leads to the problem of inaccurate detection result and low efficiency. A fault detection method for mechanical equipment based on wavelet domain Wiener filter is proposed. Firstly, combining Wiener filter with wavelet filtering, the vibration signal acquisition model of mechanical equipment is established, and the error between real signal and filtered signal is obtained. The orthogonal wavelet transform of noisy signal is combined with wavelet threshold filtering principle. The vibration signal of mechanical equipment is separated by the principle of fast independent component analysis, and the initial separation matrix of signal with unit variance is constructed, and the estimated value of source signal is obtained, and the vibration signal of mechanical equipment is collected according to the estimated value. The detection optimization of mechanical fault equipment is realized. The simulation results show that the proposed method can improve the efficiency of mechanical equipment fault detection, reduce the detection error, and have strong robustness.
【作者单位】：华北理工大学机械工程学院;
【基金】：唐山市科技计划项目(12140202A-3)
【分类号】：TH17;TP391.9

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