تقرير
Homological Time Series Analysis of Sensor Signals from Power Plants
| العنوان: | Homological Time Series Analysis of Sensor Signals from Power Plants |
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| المؤلفون: | Melodia, Luciano, Lenz, Richard |
| سنة النشر: | 2021 |
| المجموعة: | Computer Science |
| مصطلحات موضوعية: | Computer Science - Machine Learning, Electrical Engineering and Systems Science - Signal Processing |
| الوصف: | 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. Comment: Code available at https://e1.pcloud.link/publink/show?code=XZ6PJHZn98aUtBle55gARaqJAkJI0C6uWhy |
| نوع الوثيقة: | Working Paper |
| DOI: | 10.1007/978-3-030-93736-2_22 |
| URL الوصول: | http://arxiv.org/abs/2106.02493 |
| رقم الأكسشن: | edsarx.2106.02493 |
| قاعدة البيانات: | arXiv |
| DOI: | 10.1007/978-3-030-93736-2_22 |
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