基于1DCNN-BiLSTM的端到端滚动轴承故障诊断方法

针对滚动轴承早期故障诊断时时频域特征选取主观性强、时序特征信息利用不足等问题，提出一种基于卷积神经网络和双向长短时记忆神经网络的滚动轴承早期故障诊断方法。采用卷积神经网络提取原始振动信号特征，并在卷积层后引入批正则化层，以消除数据的不规则性对权重优化的影响，并通过扩展首层卷积层和调整步长以提高特征提取效率。引入双向长短时记忆神经网络提升卷积神经网络对时序特征的提取能力，通过批正则化层和Dropout层增强模型的鲁棒性和减少神经元与神经元之间的依赖关系。最后，通过滚动轴承试验数据对文中方法进行验证。结果表明：与传统方法相比，文中方法不仅训练速度更快，而且故障诊断准确率也大幅提高。

End-to-End Rolling Bearing Fault Diagnosis Method Based on 1DCNN-BiLSTM

Aiming at the problems that the selection of the time-frequency domain characteristics has strong subjectivity and the feature information about the time series is underused,a fault diagnosis method was proposed based on an improved convolutional neural network (CNN) and bi-directional long short-term memory neural network (BiLSTM) for an early rolling bearing.The convolutional neural network was used to extract the features of the original vibration signal,and a batch regularization layer was introduced after the convolutional layer to eliminate the influence of data irregularity on weight optimization.Meanwhile,the feature extraction efficiency was improved by expanding the first convolutional layer and adjusting the step size of the CNN model.The bi-directional long and short-term memory neural network was introduced to remedy the insufficiency of the convolutional neural network in term of extracting the feature information of the time series.A batch regularization layer and the dropout layer were used to enhance the robustness and reduce the interaction and the dependencies between neurons of the proposed model.Finally,the proposed model was verified by the test data of rolling bearing.The results show that compared with the traditional methods,the proposed method not only has faster training speed,but also greatly improves the fault diagnosis accuracy.