基于VMD和MRVM变负荷工况下的滚动轴承故障诊断

为了能够对变负荷工况下的轴承早期故障及损伤程度进行准确有效的诊断,提出了基于改进混沌果蝇优化算法的变分模态分解(variable mode decomposition,简称VMD)和基于嵌套一对一算法的多分类相关向量机(multi-class relevance vector machine,简称MRVM)的智能诊断模型。首先,使用改进混沌果蝇优化算法(improved chaotic fruit fly optimization algorithm,简称ICFOA)对VMD的本征模态函数(intrinsic mode function,简称IMF)个数和惩罚参数进行优化,搜索两个参数的最优组合值;其次,使用最优组合参数值对VMD算法的关键参数进行设定,并对已知的故障信号进行分解获得相应的IMF分量;然后,使用嵌套一对一算法构造高精度的多分类RVM学习模型,将IMF分量的二维边际谱熵值作为MRVM的输入特征向量;最后,使用不同载荷下的实验数据进行验证。实验结果表明,所提出的方法能够准确地对变载荷工况下的轴承故障进行诊断,其中轴承故障类型的诊断精度为100%,轴承故障程度的诊断精度为91.87%,诊断精度较高,鲁棒性强。

Fault Diagnosis of Rolling Bearing Under Variable Load Condition Based on Variable Mode Decomposition and Multi-class Relevance Vector Machine

In order to accurately and effectively diagnose bearing fault and its fault degree under the variable load conditions, variable mode decomposition (VMD) based on improved chaotic fruit fly optimization algorithm and multi-class relevance vector machine (MRVM) based on nested one to one algorithm is proposed. Firstly, the improved chaos fruit fly optimization algorithm(ICFOA) is used to optimize the penalty parameter and component numbers of intrinsic mode function (IMF) of the VMD, and search for the optimal combination of two parameters. Next, the key parameters of VMD are set by using the optimal combination parameter value, and the corresponding IMF components are obtained by decomposing the known fault signals. Then, the nested one-to-one algorithm is used to construct a high-precision multi-classification RVM learning model, and the 2-D marginal spectral entropy of IMF components are used as the input feature vector of MRVM. Finally, the experimental data under different loads are used to verify the results. The experimental results show that the proposed method can accurately diagnose bearing faults under variable load conditions, in which the diagnostic accuracy of bearing fault type is 100%, the diagnostic accuracy of bearing fault degree is 91.87%, and with high accuracy and robustness.