论文标题
增强的CNN具有全球特征，用于诊断复杂化学过程的故障诊断
Enhanced CNN with Global Features for Fault Diagnosis of Complex Chemical Processes
论文作者
论文摘要
卷积神经网络（CNN）模型已被广泛用于复杂系统的故障诊断。但是，传统的CNN型号依靠小内核过滤器来从图像中获取本地功能。因此，需要过度深的CNN来捕获全局特征，这对于动态系统的故障诊断至关重要。在这项工作中，我们提出了一个嵌入全局特征（GF-CNN）的改进的CNN。我们的方法使用多层感知器（MLP）缩小尺寸，以直接提取全局特征并将其集成到CNN中。这种方法的优点是，图像中的本地和全局模式都可以通过简单的模型体系结构来捕获，而不是建立深CNN模型。所提出的方法应用于田纳西州伊士曼进程的断层诊断。模拟结果表明，与传统CNN相比，GF-CNN可以显着改善断层诊断性能。所提出的方法也可以应用于其他领域，例如计算机视觉和图像处理。
Convolutional neural network (CNN) models have been widely used for fault diagnosis of complex systems. However, traditional CNN models rely on small kernel filters to obtain local features from images. Thus, an excessively deep CNN is required to capture global features, which are critical for fault diagnosis of dynamical systems. In this work, we present an improved CNN that embeds global features (GF-CNN). Our method uses a multi-layer perceptron (MLP) for dimension reduction to directly extract global features and integrate them into the CNN. The advantage of this method is that both local and global patterns in images can be captured by a simple model architecture instead of establishing deep CNN models. The proposed method is applied to the fault diagnosis of the Tennessee Eastman process. Simulation results show that the GF-CNN can significantly improve the fault diagnosis performance compared to traditional CNN. The proposed method can also be applied to other areas such as computer vision and image processing.
