摘要
在实际的工程应用中,电机故障发生的频率极低,电机的故障数据通常较少,正常数据与故障数据存在严重的比例失衡,这对基于数据驱动的电机故障诊断方法提出了挑战。针对这一问题,该文提出一种多源信息融合的电机故障诊断方法。首先,采用快速谱峭度的特征提取方法将电机定子电流信号和振动加速度信号转化为谱峭度特征图像;其次,搭建一种双通道残差网络模型融合振动信号和电流信号的故障特征并完成故障分类;最后,利用实验台架所采集的5种故障电机数据对多源信息融合的故障诊断方法进行了验证。研究结果表明:在故障数据严重缺失的情况下,故障诊断准确度可以达到95%以上,远高于传统的基于数据驱动的故障诊断方法,同时该方法还可以同样应用于旋转机械设备的故障诊断,具备良好的泛化性。
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.
作者
贾晗
尚前明
金华标
JIA Han;SHANG Qianming;JIN Huabiao(School of Naval Architecture Ocean And Energy Power Engineering,Wuhan University of Technology,Wuhan 430063,China)
出处
《机械科学与技术》
北大核心
2025年第5期847-856,共10页
Mechanical Science and Technology for Aerospace Engineering
基金
国家自然科学基金项目(51909200)
国家重点研发计划(2019YFE0104600)。
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
fault diagnosis
information fusion
fast spectral kurtosis
residual neural network
convolutional block attention module
