The invention discloses a multi-scale convolutional capsule network elevator fault diagnosis method based on data enhancement and an attention mechanism. The method comprises the following steps: 1, acquiring an original vibration signal of the operation acceleration of an elevator car; 2, obtaining an effective vibration signal; 3, optimizing a DCGAN network model, and introducing a spectrum normalization and space-time attention mechanism module into a discriminator and a generator to form an improved data enhancement overall model; 4, inputting the two-dimensional time-frequency feature image into the data enhancement overall model to obtain a generated new feature image; 5, constructing a multi-scale convolutional capsule network; 6, constructing an improved channel attention mechanism (CBAM) network module, and fusing the improved CBAM network module into a multi-scale convolutional capsule network; and 7, sending the enhanced feature map into a multi-scale convolutional capsule network fused with an attention mechanism to carry out elevator fault diagnosis. According to the invention, the feature information is enriched, and the response and learning ability of important features is improved.
一种基于数据增强和注意力机制的多尺度卷积胶囊网络电梯故障诊断方法,包括以下步骤:第一步:采集电梯轿厢运行加速度的原始振动信号;第二步:获取有效的振动信号;第三步:优化DCGAN网络模型,将谱归一化和时空注意力机制模块引入鉴别器和生成器,构成改进的数据增强整体模型;第四步:将二维时频特征图像输入数据增强整体模型,得到生成的新特征图像;第五步:构建多尺度卷积胶囊网络;第六步:构建改进的通道注意力机制CBAM网络模块,将其融合到多尺度卷积胶囊网络中;第七步:将增强后的特征图送入融合注意力机制的多尺度卷积胶囊网络中进行电梯故障诊断。本发明丰富了特征信息,提高了重要特征的响应和学习能力。
Multi-scale convolutional capsule network elevator fault diagnosis method based on data enhancement and attention mechanism
基于数据增强和注意力机制的多尺度卷积胶囊网络电梯故障诊断方法
2024-08-09
Patent
Electronic Resource
Chinese
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