基于PSO-VMD与贝叶斯网络的滚动轴承故障诊断

Fault diagnosis of rolling bearing based on PSO-VMD and Bayesian network

为解决电机在变负载运行条件下滚动轴承振动信号故障的特征提取困难、故障诊断准确率低的问题,提出一种基于变步长粒子群的变分模态分解与贝叶斯网络相结合的滚动轴承故障诊断模型。通过变步长粒子群算法优化的变分模态分解与Hilbert变换,提取故障信息并离散化处理,构建贝叶斯网络故障诊断模型,对滚动轴承故障发生概率推理,并利用完备、不完备数据集以及噪声试验验证该方法的准确性。仿真结果表明,该方法能高效提取特征信息,实现对不确定信息的推理估计,提高滚动轴承故障诊断的准确率,在滚动轴承的故障诊断预测中具有较好的理论与应用前景。

In order to solve the problems of difficulty in feature extraction and low fault recognition rate under variable load running condition of motor,a rolling bearing fault diagnosis model based on combination of variable mode decomposition function optimized by variable step size particle swarm and Bayesian networks was put forward.The fault information was extracted and discretized by the variational mode decomposition optimized by the variable step size particle swarm optimization algorithm and Hilbert transform.And then,a Bayesian networks diagnosis model with fault types was constructed,which inferred the probability of rolling bearing fault occurrence.The complete data set,the incomplete data set,and the noise experiments were used to verify the accuracy of the method.The simulation results showed that the proposed method could effectively extract feature information and predict estimation of uncertain information.It improved the accuracy of rolling bearing fault diagnosis,and was very promising in the fault diagnosis and prediction of rolling bearings.