Publisher DOI: | 10.1016/j.ifacol.2022.07.173 |
Title: | Using low-rank multilinear parameter identification for anomaly detection of building systems |
Language: | English |
Authors: | Schnelle, Leona Lichtenberg, Gerwald ![]() Warnecke, Christian |
Editor: | Timotheou, Stelios |
Keywords: | Parameter identification; Multilinear Models; Tensor Decomposition; Building systems; Anomaly Detection |
Issue Date: | Jul-2022 |
Publisher: | Elsevier |
Book title: | 11th IFAC Symposium on Fault Detection, Supervision and Safety for Technical Processes : SAFEPROCESS 2022 ; Proceedings |
Journal or Series Name: | IFAC-PapersOnLine |
Volume: | 55 |
Issue: | 6 |
Startpage: | 470 |
Endpage: | 475 |
Project: | Supervision und Optimierung von Neubauten durch Daten-Exploration |
Conference: | IFAC Symposium on Fault Detection, Supervision and Safety for Technical Processes 2022 |
Abstract: | The paper proposes a new method for anomaly detection based on multilinear low-rank models. No a priori knowledge about the investigated system is needed for data-driven parameter identification of these models. Multilinear parameter identification is able to cover more dynamic phenomena than linear black box identification. A minimal model of rank 1 has a tiny number of parameters which is equal ... |
URI: | http://hdl.handle.net/20.500.12738/13248 |
ISSN: | 2405-8963 |
Institute: | Department Medizintechnik Fakultät Life Sciences |
Type: | Chapter/Article (Proceedings) |
Funded by: | Bundesministerium für Bildung und Forschung |
Appears in Collections: | Publications without full text |
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