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时间特征与空间特征融合的轻量网络故障诊断方法

Lightweight network fault diagnosis method combining temporal features with spatial features

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为了解决多传感器数据间存在信息交叉、特征重复,导致模型训练精度低的问题,对滚动轴承在声辐射信号下的故障诊断进行了研究,提出了一种时间特征与空间特征融合的轻量网络故障诊断(SF-TFNet)方法.首先,利用卷积神经网络提取了原始轴承声阵列信号的空间特征(SFs),使用长短时记忆网络(LSTM)提取了声阵列信号中的时域特征(TFs),并对提取的SFs和TFs进行了特征融合,生成了新的特征矩阵;然后,为了消除融合特征带来的重叠特征和信息冗余问题,引入了基于核的主成分分析(KPCA)方法对新生成的特征矩阵进行了非线性降维,去除了特征中的冗余成分,构建了滚动轴承新的时空特征数据集;最后,采用AdaBoost算法对新生成的数据集进行了故障分类,并得到了滚动轴承的最终故障诊断结果.研究结果表明:在半消声室滚动轴承故障实验台测试中,SF-TFNet方法的故障分类精度可以达到 99.75%,其分类精度较高、聚类效果明显.在强背景噪声环境下与 ResNet、ICNN和AlexNet三种方法进行比较,SF-TFNet方法不仅收敛速度快,而且故障识别精度高,诊断精度最高可达 99.25%.为基于多通道的滚动轴承声辐射信号故障诊断提供了理论依据.
In order to solve the problem of low model training accuracy caused by cross information and repeated features among multi-sensor data,the fault diagnosis of rolling bearing under acoustic radiation signal was studied.A lightweight network fault diagnosis method based on the fusion of spatial and temporal features(SF-TFNet)was proposed.Firstly,convolutional neural network was used to extract space features(SFs)of the original bearing acoustic array signal,and then long short-term memory(LSTM)was used to extract time features(TFs)in the acoustic array signal,and the extracted SFs and TFs were fused to generate a new feature matrix.In order to eliminate the overlapping features and information redundancy caused by fusion features,kernel principal component analysis(KPCA)method was introduced to reduce the dimensionality of the newly generated feature matrix,remove the redundant components of the features,and construct a new spatio-temporal feature data set of rolling bearings.Finally,AdaBoost algorithm was used to classify the faults of the newly generated data set,and the final fault diagnosis result of the rolling bearing was obtained.The research results show that,on the rolling bearing fault test bench in semi-anechoic chamber,the SF-TFNet method can achieve a high fault classification accuracy of 99.75%,and the clustering effect of the proposed method is obvious.Comparing with ResNet,ICNN and AlexNet in the strong background noise environment,the SF-TFNet method not only converges quickly,but also has the highest accuracy,which can reach 99.25%.The comparison results further show that the proposed method has high fault identification accuracy.It provides a theoretical basis for fault diagnosis of multi-channel acoustic radiation rolling bearing.

rolling bearingacoustic radiation signalmulti-information fusionlight weight feature fusionfault diagnosislong short-term memory(LSTM)time features(TFs)kernel principal component analysis(KPCA)

王仲、姜娇、张磊、谷泉、赵新光

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辽宁科技学院 机械工程学院,辽宁 本溪 117004

滚动轴承 声辐射信号 多信息融合 特征轻量融合 故障诊断 长短时记忆网络 时域特征 基于核的主成分分析

辽宁省自然科学基金计划项目辽宁省教育厅高等学校基本科研面上项目

2022-BS-296LJKMZ20221690

2024

10.3969/j.issn.1001-4551.2024.09.005

机电工程
浙江大学　浙江省机电集团有限公司

机电工程

CSTPCD北大核心
影响因子：0.785
ISSN：1001-4551
年,卷(期)：2024.41(9)
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