Automatic Time Series Segmentation as the Basis for Unsupervised, Non-Intrusive Load Monitoring of Machine Tools
| Autor: | J. Johst, T. Weiß, J-P. Seevers, Henning Meschede, Jens Hesselbach |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
0209 industrial biotechnology
Dynamic time warping business.product_category energy management Betriebszustand Computer science 02 engineering and technology Intelligenter Sensor 010501 environmental sciences computer.software_genre unsupervised learning 01 natural sciences load profile analysis 020901 industrial engineering & automation Component (UML) Time-series segmentation Fertigung Energiemanagement 0105 earth and related environmental sciences General Environmental Science Nachhaltigkeit operational state identification sustainable manufacturing intelligent sensor Gear manufacturing Machine tool Lastprofil Unüberwachtes Lernen General Earth and Planetary Sciences Data mining Noise (video) business computer Energy (signal processing) Efficient energy use |
| DOI: | 10.17170/kobra-202012032328 |
| Popis: | Detailed energy monitoring and benchmarking at the individual component level is necessary to increase energy efficiency in complex production systems. Non-intrusive load monitoring (NILM) provides an economical solution for operational state detection and load disaggregation without the need for large-scale use of fine-grained energy meters. Existing supervised NILM approaches require detailed training data including control information about individual devices. Unsupervised approaches, on the other hand, often require high measurement resolution and are faced with the problem of detecting continuous states. This paper proposes a simple step-by-step, completely unsupervised NILM approach that distinguishes between almost constant and non-constant segments with flexible segment lengths. Taking into account various electrical parameters and their statistical moments, hierarchical density-based spatial clustering of applications with noise (HDBScan) is applied to constant segments. The analysis of non-constant segments is based on agglomerative hierarchical clustering and dynamic time warping. Based on real energy monitoring from a gear manufacturing system we show the applicability of our methodology and discuss how it can be combined with existing NILM techniques. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|