首页|基于1DCNN-BiLSTM的端到端滚动轴承故障诊断方法

基于1DCNN-BiLSTM的端到端滚动轴承故障诊断方法

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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 fea-ture 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.

rolling bearingfault diagnosisconvolutional neural network(CNN)bi-directional long short-term memory neural network(BiLSTM)

徐行、李军星、贾现召、邱明

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河南科技大学机电工程学院,河南洛阳 471003

机械装备先进制造河南省协同创新中心,河南洛阳 471003

滚动轴承 故障诊断 卷积神经网络(CNN) 双向长短时记忆神经网络(BiLSTM)

国家自然科学基金青年科学基金项目河南省科技研发计划联合基金青年科学家项目河南省科技研发计划联合基金应用攻关项目河南省高校科技创新人才支持计划项目河南省高等学校青年骨干教师培养计划项目河南省青年人才托举工程项目

5200515922520081007323210381004324HASTIT0432021GGJS0482023HYTP050

2024

10.3969/j.issn.1001-3881.2024.11.031

机床与液压
中国机械工程学会 广州机械科学研究院有限公司

机床与液压

CSTPCD北大核心
影响因子：0.32
ISSN：1001-3881
年,卷(期)：2024.52(11)