In this paper, we use topological data analysis techniques to construct a suitable neural network classifier for the task of learning sensor signals of entire power plants according to their reference designation system. We use representations of persistence diagrams to derive necessary preprocessing steps and visualize the large amounts of data. We derive deep architectures with one-dimensional convolutional layers combined with stacked long short-term memories as residual networks suitable for processing the persistence features. We combine three separate sub-networks, obtaining as input the time series itself and a representation of the persistent homology for the zeroth and first dimension. We give a mathematical derivation for most of the used hyper-parameters. For validation, numerical experiments were performed with sensor data from four power plants of the same construction type.

翻译：在本文中,我们使用地形数据分析技术来建造一个合适的神经网络分类器,用于根据整个发电厂的参照命名系统来学习其感应信号。我们使用持久性图的表示法来得出必要的预处理步骤和可视化大量数据。我们用单维的进化层和堆叠的长期短期记忆作为适合处理持久性特征的残余网络来生成深层结构。我们把三个单独的子网络结合起来,作为输入,获取时间序列本身和代表零和一维的持久性同系物。我们用数学推算了大部分使用的超参数。为了验证,用同一类型四个发电厂的传感器数据进行了数字实验。