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基于卷积自编码器的座椅电机故障诊断

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故障数据的缺失一直是制约设备故障诊断发展的重要因素,现有研究通过刻意损坏设备的方法来采集故障数据.为实现座椅电机的无损故障诊断,文章对座椅电机的故障机理进行分析,确定可能发生的故障类型,通过在座椅电机表面粘贴微型喇叭并播放故障声音,来模拟故障的发生.在自编码器系统的基础上,引入卷积操作,使用卷积层代替全连接层,通过输入数据维度、卷积核的尺寸和数量以及池化、正则化等操作对模型结构进行调整.采用IDMT Isa Electric Engine数据集作为源域数据,对模型进行预训练.使用迁移学习方法将源域中已经学习到的数据分布迁移到座椅电机故障诊断任务中,并与各类模型检测结果进行对比.结果显示,文中方法在召回率保持1.00的情况下,曲线下面积达到0.86,检测结果可靠,具有实际应用价值.
Fault diagnosis of seat motor based on convolutional autoencoder
Lack of fault data is always an important factor restricting the development of equipment fault diagnosis.Existing researches collect fault data by deliberately damaging equipment.In order to realize nondestructive fault diagnosis of seat motor,the fault mechanism of seat motor is analyzed to determine the possible types of faults in this paper.The occurrence of fault is simulated by sticking micro-speakers pasted on the surface of seat motor to play fault sound.On the basis of self-encoder system,the convolution operation is introduced,and the fully connected layer is replaced with convolution layer.The model structure is adjusted with the input data dimension,the size and number of convolution kernels,pooling and regularization.IDMT Isa Electric Engine data set is used as the source domain data to pre-train the model.Then,the learned data distribution in the source domain is transferred to the seat motor fault diagnosis task,and the detection results of various models are compared.The results show that under the condition of keeping the recall rate 1.00,the area under the curve(AUC)of the proposed method reaches 0.86.It manifests that the detection result is reliable,and the proposed method could be practically applied.

convolutional autoencoderunsupervised learninganalog fault soundtransfer learning

王龙祥、朱亚伟、卢炽华、刘志恩、肖文浩、谢丽萍

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武汉理工大学现代汽车零部件技术湖北省重点实验室,湖北武汉 430070

佛山仙湖实验室工业软件与仿真技术实验室,广东佛山 528200

卷积自编码器 无监督学习 模拟故障声音 迁移学习

国家自然科学基金

52175111

2024

声学技术
中科院声学所东海研究站,同济大学声学所,上海市声学学会,上海船舶电子设备研究所

声学技术

CSTPCD北大核心
影响因子:0.415
ISSN:1000-3630
年,卷(期):2024.43(3)
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