摘要
针对用于粮食加工的抛光机在实际运行环境中信号复杂且具有非平稳性等问题,本研究提出了一种基于深度时序特征融合网络(DSTFN-VMDNR)的故障诊断方法。首先,利用变分模态分解(VMD)对原始信号进行降噪,从中提取出多个平稳的模态分量。其次,通过时序网络(TimesNet)结合双向长短期记忆网络(BiLSTM)提取深层多尺度特征。随后,引入卷积残差块进一步增强特征提取的能力。最后,采用全局平均池化(GAP)层对提取的特征进行汇聚,以减少参数数量并提升模型的泛化能力。在美国凯斯西储大学和中国江南大学的两个数据集上进行验证,模型的准确率分别为99.75%和96.87%。结果表明,该方法在抛光机轴承故障诊断方面具有显著优势。
Aiming at the polishing machines for grain processing in the actual operating environment of the signal complexity and non-stationarity and other issues,this research proposes a fault diagnosis method based on Deep Temporal Feature Fusion Network(DSTFN-VMDNR).The process begins with Variational Mode Decomposition(VMD),which denoises the original signal and extracts multiple stationary modal components.Deep temporal features and multi-scale features are then extracted using the Time-Series Network(TimesNet)combined with a Bi-directional Long Short-Term Memory(BiLSTM)network.A convolutional residual block is incorporated to further enhance feature extraction capability.Finally,the extracted features are aggregated using a Global Average Pooling(GAP)layer.The model achieves accuracies of 99.75%on the CWRU dataset and 96.87%on the Jiangnan University dataset,thereby demonstrating the effectiveness of the method for bearing fault diagnosis.
作者
黄佳文
蔡华锋
HUANG Jia-wen;CAI Hua-feng(School of Electrical&Electronic Engineering,Hubei University of Technology,Wuhan,Hubei 430068,China)
出处
《粮油食品科技》
北大核心
2025年第5期154-161,共8页
Science and Technology of Cereals,Oils and Foods
基金
高校产学研创新基金“基于深度学习的稻谷加工设备故障诊断”(2021ITA05025)。
关键词
变分模态分解
平稳模态分量
轴承故障诊断
时序网络
双向长短期记忆网络
variational modal decomposition
stationary modal components
bearing fault diagnosis
TimesNet
Bi-directional Long Short-Term Memory networks
