基于DEMD和对称差分能量算子解调的滚动轴承故障诊断

摘要/Abstract

摘要：

针对机械故障振动信号多为调制信号的特点，为了更好地提取多分量调幅调频信号的幅值和频率信息，提出了基于微分的经验模式分解(DEMD)与对称差分能量算子相结合的解调方法。利用DEMD算法将原始振动信号进行分解，得到若干个单分量信号；对每一个单分量信号进行三点对称差分能量算子解调，得到各单分量信号的瞬时幅值和瞬时频率，并计算出包络谱。将该方法应用于仿真信号和滚动轴承故障信号的诊断，实验结果表明，该方法能有效地提取机械故障信号的故障特征，实现旋转机械故障诊断。

关键词: 微分经验模式分解, 对称差分能量算子, 滚动轴承, 故障诊断

Abstract:

Aimed at the modulated characteristics of the mechanical fault vibration signals, in order to extract amplitude and frequency informations of multi-component amplitude modulation and frequency modulation signals, a method was put forward based on the DEMD combining with symmetric difference energy operator demodulation. First of all, the original vibration signals were decomposed by DEMD algorithm, getting a number of single component signal, and then symmetric difference of three energy operator demodulation was used for every single component signal, getting instantaneous amplitude and instantaneous frequency of each single component signal, and the spectral envelope was calculated, finally the method was applied to the simulation signals and the rolling bearing fault signals. The experimental results show that the method can accurately extract the fault features of mechanical fault signals and realize rotating machinery fault diagnosis effectively.

Key words: differential-based empirical mode decomposition(DEMD), symmetric difference energy operator, rolling bearing, fault diagnosis

中图分类号:

孟宗;季艳. 基于DEMD和对称差分能量算子解调的滚动轴承故障诊断[J]. 中国机械工程, 2015, 26(12): 1658-1664.

Meng Zong;Ji Yan. Fault Diagnosis of Rolling Bearings Based on DEMD and Symmetric Difference Energy Operator Demodulation[J]. China Mechanical Engineering, 2015, 26(12): 1658-1664.

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