An Improved CEEMDAN Fault Diagnosis Algorithm and its Application in Machining Equipment
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摘要: 轴承的故障诊断是保证设备安全运行的重要手段。故障诊断的关键是振动信号解调的方法。自适应噪声完备集合经验模态分解（CEEMDAN）是一种自适应信号处理方法，在非线性非平稳信号中有较好的解调性能。本文提出一种基于峭度准则改进的CEEMDAN故障诊断算法。具体步骤如下：首先，采用基于峭度准则改进的CEEMDAN方法提取有用的模态分量信号；之后，将筛选出来的模态信号叠加并通过Teager能量算子得到输出的能量信号；最后，对信号进行包络谱分析提取故障特征频率，从而实现故障诊断。通过仿真和加工装备部件的试验验证，改进的方法在实际应用中具有一定的实用价值。
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关键词:
- CEEMDAN /
- 峭度准则 /
- Teager能量算子 /
- 故障诊断 /
Abstract: The fault diagnosis of bearing is an important mean to ensure the safe operation of the equipment. The key of fault diagnosis is the method of vibration signal demodulation. Adaptive noise complete set empirical mode decomposition (CEEMDAN) is an adaptive signal processing method, which has good demodulation performance in nonlinear and non-stationary signals. In this paper, an improved CEEMDAN fault diagnosis algorithm based on kurtosis criterion was proposed. First, using the improved CEEMDAN method based on kurtosis criteria to extract useful modal component signals. After that, the selected modal signals are superimposed and the output energy signals are obtained through the Teager operator. Finally, the fault feature frequency is extracted from the envelope spectrum analysis, and the fault diagnosis is realized. The improved method has a certain practical value in practical application through the test of simulation and processing equipment parts. -
表 2 各IMF分量峭度值
IMF分量 峭度值 IMF1 3.314 1 IMF2 3.127 1 IMF3 2.725 0 IMF4 3.521 6 IMF5 3.161 4 IMF6 2.958 8 IMF7 3.375 2 IMF8 3.315 6 IMF9 2.240 1 IMF10 2.118 2 IMF11 2.090 4 IMF12 1.985 6 IMF13 1.908 96 IMF14 2.819 0 
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