A Novel Fault Diagnosis Method of Gearbox Based on Maximum Kurtosis Spectral Entropy Deconvolution
A Novel Fault Diagnosis Method of Gearbox Based on Maximum Kurtosis Spectral Entropy Deconvolution
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DOI：
10.1109/access.2019.2900503
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发表时间：
2019-01-01
期刊：
影响因子：
3.9
通讯作者：
He, Gaofeng
中科院分区：
文献类型：
--
作者：
Wang, Zhijian;Zhou, Jie;He, Gaofeng
最小熵反卷积（MED）由于能增强冲击信号的能量，在齿轮箱故障诊断中得到了广泛的应用。但对单个异常脉冲振荡较为敏感。这是因为它以峰度为目标函数，通过迭代求解最优滤波器。此外，滤波器长度不能自适应，需要人工确定。提出了一种最大峰度谱熵反褶积方法，并将其应用于轴承故障诊断。考虑到峰度谱熵具有突出连续冲击振荡的优点，选择峰度谱熵作为反褶积的目标函数。同时，利用峰度谱熵作为改进局部粒子群优化算法（LPSO）的适应度函数，对滤波器长度进行优化，使得MKSED在求解反卷积的同时自适应确定滤波器长度，从而能够准确提取连续脉冲信号。仿真信号分析结果表明，所提出的MKSED方法优于MED方法，并将该方法应用于轴承故障诊断，验证了其提取连续冲击的能力。
Minimum entropy deconvolution (MED) is widely used in the gearbox fault diagnosis because it can enhance the energy of the impact signal. However, it is sensitive to single abnormal impulsive oscillation. This is because it takes kurtosis as the objective function and solves the optimal filter by iteration. In addition, the filter length is not adaptive and needs to be determined artificially. This paper proposes a maximum kurtosis spectral entropy deconvolution (MKSED) method and applies it to bearing fault diagnosis. Considering that the kurtosis spectral entropy has the advantage of highlighting the continuous impact oscillation, the kurtosis spectral entropy is chosen as the objective function of deconvolution. At the same time, kurtosis spectral entropy is also used as the fitness function of improved local particle swarm optimization algorithm (LPSO), and the filter length is optimized by LPSO, which makes that MKSED adaptively determines the length of the filter while solving the deconvolution, so that it can accurately extract the continuous pulse signal. The results of the simulation signal analysis show that the proposed MKSED method is superior to MED, and the proposed method is applied to bearing fault diagnosis, which verifies its ability to extract continuous impact.