基于敏感分量与MCPG的轴承故障诊断方法

Bearing Fault Diagnosis Method based on Sensitive Component and MCPG

针对滚动轴承故障难以准确识别问题,提出了一种基于敏感分量与多卷积池化组(Multi convolution pooling group,MCPG)的故障诊断方法。首先,采用经验模态分解(Empirical mode decom⁃position,EMD)将原始信号分解成为多个固有模态分量(Intrinsic mode function,IMF),使用离散Fréchet距离作为衡量指标,选取出故障敏感分量作为表征不同故障类型的故障数据源;之后,提出了一种MCPG深度神经网络架构,并使用敏感数据源对模型进行训练与测试,从而实现数据驱动的轴承故障诊断。通过实验验证,表明该方法对不同类型的振动数据(不同转速、不同损伤类型、不同损伤程度)均具有较好的识别效果。

Aiming at the problem that is difficult to accurately identify rolling bearing faults,a fault diag⁃nosis method based on sensitive components and Multi Convolution Pooling Group(MCPG)is proposed.First⁃ly,the Empirical Mode Decomposition(EMD)is used to decompose the original signal into multiple Intrinsic Mode Function(IMF),and the discrete Fréchet distance is used as the measurement index,the fault sensitive components are selected as the fault data sources representing different fault types.Then,a MCPG deep neural network architecture is proposed,and sensitive data sources are used to train and test the model to achieve the data-driven bearing fault diagnosis.Through experimental verification,it is proved that the method has good recognition effect on different types of vibration data(different speeds,different damage types,different dam⁃age degrees).