论文标题
使用1D-CNN根据传感器值检测生产阶段
Detecting Production Phases Based on Sensor Values using 1D-CNNs
论文作者
论文摘要
在工业4.0的背景下，从传感器信息中提取知识起着重要作用。通常，从传感器值收集的信息揭示了生产水平（例如异常或机器状态）的有意义的见解。在我们的用例中，我们在卷积神经网络的帮助下通过检查传感器值来确定生产阶段。数据集源于用于金属热处理的回火炉。我们的监督学习方法揭示了用于检测生产阶段的选定神经网络的有希望的准确性。我们将这项工作中所示的解决方案视为预测维护领域的显着支柱。
In the context of Industry 4.0, the knowledge extraction from sensor information plays an important role. Often, information gathered from sensor values reveals meaningful insights for production levels, such as anomalies or machine states. In our use case, we identify production phases through the inspection of sensor values with the help of convolutional neural networks. The data set stems from a tempering furnace used for metal heat treating. Our supervised learning approach unveils a promising accuracy for the chosen neural network that was used for the detection of production phases. We consider solutions like shown in this work as salient pillars in the field of predictive maintenance.
