Homological Time Series Analysis of Sensor Signals from Power Plants

Melodia L, Lenz R (2022)

Publication Language: English

Publication Type: Conference contribution, Conference Contribution

Publication year: 2022

Publisher: Springer

Series: Communications in Computer and Information Science

City/Town: Cham

Book Volume: 1524

Pages Range: 283-299

Conference Proceedings Title: Machine Learning and Principles and Practice of Knowledge Discovery in Databases

Event location: Bilbao ES

ISBN: 978-3-030-93733-1

URI: https://link.springer.com/book/10.1007/978-3-030-93736-2

DOI: 10.1007/978-3-030-93736-2\_22

Abstract

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.

Authors with CRIS profile

Luciano Melodia Professur für evolutionäres Datenmanagement Richard Lenz Professur für evolutionäres Datenmanagement

Related research project(s)

Schema Inference and Machine Learning (SIML) Aug. 1, 2018 - Aug. 1, 2021

How to cite

APA:

Melodia, L., & Lenz, R. (2022). Homological Time Series Analysis of Sensor Signals from Power Plants. In Springer (Eds.), Machine Learning and Principles and Practice of Knowledge Discovery in Databases (pp. 283-299). Bilbao, ES: Cham: Springer.

MLA:

Melodia, Luciano, and Richard Lenz. "Homological Time Series Analysis of Sensor Signals from Power Plants." Proceedings of the Machine Learning for Irregular Time Series, Bilbao Ed. Springer, Cham: Springer, 2022. 283-299.

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