基于视听融合的宽频带振动频谱提取

Extraction of Broadband Vibration Spectrum Based on Audio‑Visual Fusion

提取振动频谱对于旋转机械的故障诊断至关重要。环境和噪声的多样化限制了传统单模态振动提取方法的性能。由于视听信号具有不同的采样频率、噪声和环境限制,视听融合算法可以有效解决单一模态存在的问题。基于此,文中提出了一种基于视听融合深度卷积神经网络的宽带频谱提取方法,该方法充分融合了不同模态的有效信息。该模型基于双流编码器从不同的模态中提取特征,使用深度残差融合模块提取高级融合特征并输出给解码器。实验结果表明,该模型的表现优于最新的振动提取方法,如RegNet,MFCNN及L2L等,噪声环境下的振动频谱提取准确率提高15%。

Vibration spectrum extraction is essential for fault diagnosis of rotating machinery.Environmental diversification and the presence of noise limit the performance of traditional single-modal vibration extraction methods.Since visual and audio signals have different sampling frequencies,noise and environmental constraints,audio-visual fusion can effectively solve the problem caused by single modality.Based on this,this paper proposes a wideband spectrum extraction method based on an audio-visual fusion deep convolutional neural network,which fully fuses the effective information of different modalities to complement each other.The proposed model uses a dual-stream encoder to extract features from different modalities,and a deep residual fusion module extracts high-level fusion features and feeds them to the decoder.The experimental results show that the performance of this model is superior to the latest vibration extraction methods,and the proposed model outperforms other state-of-the-art models such as RegNet,MFCNN,and L2L,which improves the accuracy of vibration spectrum extraction by 15%in noisy environment.