机械科学与技术2025，Vol.44Issue(5)：847-856,10.DOI:10.13433/j.cnki.1003-8728.20230234
多源信息融合的电机小样本故障诊断
A Small-sample Motor Fault Diagnosis Method Based on Multi-source Information Fusion
摘要
Abstract
In practical engineering applications,the frequency of faults of a motor is extremely low.Usually there is a lack of its fault data and a serious imbalance between normal data and fault data,which poses a challenge to the data-driven motor fault diagnosis.In order to solve this problem,the paper proposes a motor fault diagnosis method based on multi-source information fusion.Firstly,the fast spectral kurtosis feature extraction method is used to convert the motor's stator current signal and vibration acceleration signal into its spectral kurtosis feature image.Secondly,a dual-channel residual neural network model is constructed and used to integrate the fault characteristics of the vibration signal and the current signal and to complete fault classification.Finally,the fault diagnosis method based on multi-source information fusion is verified with the five fault motor datasets collected by the experimental bench.The results show that in the case of serious lack of fault data,the fault diagnosis accuracy can reach more than 95％,which is much higher than the traditional data-driven fault diagnosis method.The method is also applicable to the fault diagnosis of rotational machinery.关键词
故障诊断/信息融合/快速谱峭度法/残差神经网络/卷积注意力模块Key words
fault diagnosis/information fusion/fast spectral kurtosis/residual neural network/convolutional block attention module分类
机械工程引用本文复制引用
贾晗,尚前明,金华标..多源信息融合的电机小样本故障诊断[J].机械科学与技术,2025,44(5):847-856,10.基金项目
国家自然科学基金项目(51909200)与国家重点研发计划(2019YFE0104600) （51909200）
