首页|分布外样本干扰下基于改进半监督原型网络的齿轮箱跨域故障诊断

分布外样本干扰下基于改进半监督原型网络的齿轮箱跨域故障诊断

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实际工程中难以获取充足可用且同分布的齿轮箱故障样本,此外,获取的无标签样本难免会混入一些分布外的未知干扰样本,这些将给现有的齿轮箱智能故障诊断研究带来难题.提出一种改进半监督原型网络,面向分布外样本干扰,实现少样本下不同工况间的齿轮箱跨域故障诊断.首先,设计一种标签分配准则,既可以充分挖掘利用无标签样本信息,为无标签样本赋予伪标签,同时也可以有效抑制分布外未知样本的干扰.然后,定义一种基于标签平滑和度量缩放的修正代价函数,可以灵活有效地评估故障样本之间的相似性,挖掘元学习任务的通用特性,进一步提高模型泛化性.将所提方法用于分析不同健康状态的齿轮箱试验数据,并设置不同的少样本跨域诊断场景和分布外干扰样本进行对比验证.试验结果表明,相比现有方法,所提方法可以更有效地实现少样本下不同工况间的齿轮箱跨域故障诊断.
Improved Semi-supervised Prototype Network for Cross-domain Fault Diagnosis of Gearbox under Out-of-distribution Interference Samples
In practical engineering,it is difficult to acquire sufficient available fault samples of gearbox with the same distribution,in addition,the acquired unlabelled samples will inevitably be mixed with some out-of-distribution unknown interference samples,which will bring challenges to the existing research on intelligent fault diagnosis of gearbox.A new method based on an improved semi-supervised prototype network is proposed for cross-domain fault diagnosis of gearbox between different working conditions under out-of-distribution interference samples.First,a label allocation criterion is designed,which can fully exploit the information and assign pseudo-labels of the unlabelled samples to effectively suppress out-of-distribution interference samples.Then,a modified cost function is defined based on label smoothing and metric scaling to fully evaluate the similarity between fault samples,which can exploit the generic characteristics of the meta-learning task,and further improve network generalisation.The proposed method is used to analyse the experimental data of gearbox under different health states,and then different few-shot cross-domain diagnosis scenarios and out-of-distribution distribution samples are set up for comparison and verification.The experimental results show that the proposed method can more effectively achieve the cross-domain fault diagnosis of gearbox under different working conditions with few samples compared with the existing methods.

improved semi-supervised prototype networkgearbox fault diagnosisout-of-distribution interference sampleslabel allocation criterionmodified cost function

邵海东、林健、闵志闪、明宇航

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湖南大学机械与运载工程学院 长沙 410082

改进半监督原型网络 齿轮箱故障诊断 分布外干扰样本 标签分配准则 修正代价函数

国家自然科学基金湖南省科技创新项目湖南省自然科学基金优秀青年科学基金

522751042023RC30972021JJ20017

2024

10.3901/JME.2024.04.212

机械工程学报
中国机械工程学会

机械工程学报

CSTPCD北大核心
影响因子：1.362
ISSN：0577-6686
年,卷(期)：2024.60(4)
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