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Applied Machine Learning: Reconstruction of Spectral Data for the Classification of Oil-Quality Levels

Stang, Marco; Böhme, Martin; Sax, Eric

Abstract (englisch):
In modern complex systems and machines - e.g., automobiles or construction vehicles - different versions of a "Condition Based Service" (CBS) are deployed for maintenance and supervision. According to the current state of the art, CBS is focusing on monitoring of static factors and rules. In the area of agricultural machines, these are for example operating hours, kilometers driven or the number of engine starts. The decision to substitute hydraulic oil is determined on the basis of the factors listed. A data-driven procedure is proposed instead to leverage the decision-making process. Thus, this paper presents a method to support continuous oil monitoring with the emphasis on artificial intelligence using real-world spectral oil-data. The reconstruction of the spectral data is essential, as a complete spectral analysis for the ultraviolet and visible range is not available. Instead, a possibility of reconstruction by sparse supporting wavelengths through neural networks is proposed and benchmarked by standard interpolation methods. Furthermore, a classification via a feed-forward neural network with the conjunction of Dynamic Time Warping (DTW) algorithm for the production of labeled data was developed. ... mehr

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Verlagsausgabe §
DOI: 10.5445/IR/1000095978
Veröffentlicht am 25.06.2019
Coverbild
Zugehörige Institution(en) am KIT Institut für Technik der Informationsverarbeitung (ITIV)
Publikationstyp Proceedingsbeitrag
Jahr 2019
Sprache Englisch
Identifikator ISSN: 2602-3199
KITopen-ID: 1000095978
Erschienen in 5th International Conference on Research in Engineering, Technology and Science (ICRETS 2019), Lissabon, P, February 3-7, 2019
Veranstaltung 5th International Conference on Research in Engineering, Technology and Science (ICRETS 2019), Lissabon, Portugal, 03.02.2019 – 07.02.2019
Verlag ISRES Publishing, Ames, IA
Seiten 1-13
Serie The Eurasia Proceedings of Science, Technology, Engineering & Mathematics (EPSTEM) ; 5
Vorab online veröffentlicht am 21.06.2019
Externe Relationen Abstract/Volltext
Schlagworte Machine learning, Neural networks, Spectral analysis
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