基于POVMD和CAF的低转速齿轮箱故障诊断

针对低转速齿轮箱齿轮故障特征频率低、故障特征频率易被背景噪声淹没,使其难以准确提取的问题,提出了基于参数优化的变分模态分解(parameter optimization variational mode decomposition,简称POVMD)和循环自相关函数(cyclic autocorrelation function,简称CAF)结合的故障诊断方法。首先,通过POVMD对原始信号进行分解,选用余弦相似度度量选取敏感的本征模态函数(intrinsic mode function,简称IMF);其次,计算其循环自相关函数谱,获得包含调制特征的循环自相关函数谱切片;最后,使用Teager能量算子(Teager energy operator,简称TEO)算法对切片解调,提取故障特征频率。同时将本方法与相关方法进行了对比分析,特征频率提取效果更加显著,仿真信号和实验数据分析验证了该方法的有效性和可靠性。

Low Speed Gearbox Fault Diagnosis Based on POVMD and CAF

The gears of low-speed have low frequency characteristics of faults. The frequency of fault is covered by background noise, and the fault signals are difficult to be accurately demodulated and extracted. In this paper, a parameter optimization-based variational algorithm decomposition (POVMD) and a cyclic autocorrelation function (CAF) diagnosis method are proposed to solve this problem. First, the original signal is decomposed by POVMD, and the cosine similarity is used to select the sensitive intrinsic mode function (IMF). Secondly, the spectrum of the sensitive components of the cyclic autocorrelation function is calculated to gain the slice of the spectrum of the autocorrelation function. Finally, Teager energy operator (TEO) is used to extract the fault feature frequency in the spectrum of instantaneous amplitude of the slice. This method is compared with related methods. The feature extraction effect is more significant. Simulation signal and experimental data analysis verify the validity and reliability of the proposed method.