考虑到在嘈杂的噪音环境中，货车滚动轴承的复合故障特征相对模糊，并且各个故障特征之间的相互影响导致了复合故障特征的有效区分困难，提出了基于李雅普诺夫指数(largest lyapunov exponents, LLE)的全变分滤波(total variation filtering，TVF)和加入排列熵(permutation entropy, PE)的多点最优最小熵解卷积(multipoint optimal minimum entropy deconvolution adjusted, MOMEDA)的滚动轴承复合故障诊断方法。首先对复合故障信号进行噪声分析，根据信号存在噪声大小来确定李雅普诺夫指数和信号混沌性，同时将惩罚设置为合适的正则化参数从而实现对复杂环境噪声的自适应降噪，然后通过排列熵改变MOMEDA中的滤波器长度对故障信号进行解卷积运算，分离出不同的故障特征，对信号作傅里叶变换提取故障特征频率，最后利用Teager能量算子(teager energy operator)增强解卷积后的故障冲击信号，实现滚动轴承复合故障的精确判别。通过将此方法应用于仿真信号模拟滚动轴承复合故障以及实际货车轴承复合故障进行验证，结果表明此方法可以实现复合故障特征的准确分离，成功识别出故障类型。
Abstract
Considering that the composite fault characteristics of freight car rolling bearings are relatively fuzzy in noisy environments, and the mutual influence between various fault features makes it difficult to effectively distinguish the composite fault features, a total variation filtering (TVF) based on the largest Lyapunov exponents (LLE) and permutation entropy (PE) are proposed A rolling bearing composite fault diagnosis method based on multi-point optimal minimum entropy deconvolution adjusted (MOMEDA). Firstly, noise analysis is performed on the composite fault signal, and the Lyapunov exponent and signal chaos are determined based on the presence of noise in the signal. At the same time, the penalty is set to appropriate regularization parameters to achieve adaptive noise reduction in complex environments. Then, the fault signal is deconvoluted by changing the filter length in MOMEDA by permutation entropy, and different fault features are separated, Perform Fourier transform on the signal to extract fault characteristic frequencies, and finally use the Teager energy operator to enhance the deconvoluted fault impact signal, achieving accurate discrimination of composite faults in rolling bearings. By applying this method to simulate composite faults of rolling bearings in simulation signals and actual composite faults of freight car bearings, the results show that this method can accurately separate the characteristics of composite faults and successfully identify the types of faults.
关键词
货车轴承；故障诊断；故障监测；特征提取；李雅普诺夫函数 /
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Key words
Truck bearings /
Fault diagnosis /
Fault monitoring /
Feature extraction /
Lyapunov function
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