基于VMD-DSSC的轴承故障诊断

Bearing Fault Diagnosis Based on VMD-DSSC

针对大数据背景下轴承故障诊断中有标签样本少的问题,设计了深度半监督小样本分类器并用于轴承故障诊断。首先,采用受限玻尔兹曼机构建深度学习分类器,利用无标签样本完成参数预训练,结合有标签样本调优参数,获得准确的分类模型。然后,改进激活函数,解决梯度消失问题,提高收敛速度,增强分类性能。最后,利用变分模态分解将信号分解为一系列本征模态函数,并提取样本熵作为故障特征输入至分类器实现故障模式识别。轴承故障诊断实验及风电机组高速轴承故障诊断结果表明,深度半监督小样本分类器能够充分利用无标签样本及有标签样本实现轴承故障模式识别,并具有故障诊断准确率高、稳定性好、耗时少、实时性好等优点,为轴承故障诊断提供了一种新方法,亦可用于数据清洗,为智能故障诊断提供有效样本。

In response to the problem of limited labeled samples in bearing fault diagnosis under the background of big data,a deep semi-supervised small sample classifier(DSSC)was designed and used for bearing fault diagnosis.Firstly,a deep learning classifier is constructed using a constrained Boltzmann mechanism,parameter pre-training is completed using unlabeled samples,and parameter tuning is combined with labeled samples to obtain an accurate classification model.Then,the activation function is improved to solve the gradient vanishing problem,improve convergence speed,and enhance classification performance.Finally,the signal was decomposed into a serial of intrinsic mode functions by variational mode decomposition,and sample entropies were calculated as the features,which were input to the classifier to recognize the fault types.The results of bearing fault diagnosis experiment and high-speed bearing fault diagnosis of wind turbines show that DSSC can fully utilize unlabeled and labeled samples to achieve bearing fault pattern recognition,and has advantages such as high fault diagnosis accuracy,good stability,less time consumption,and good real-time performance.It provides a new method for bearing fault diagnosis and can also be used for data cleaning,providing effective samples for intelligent fault diagnosis.