| | | |

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

#### Marco STANG [1] , Martin BOHME [2] , Eric SAX [3]

##### 31 28

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. Conclusively, the extent to which changes in hyper-parameters (number of hidden layers, number of neurons, weight initialization) affect the accuracy of the classification results have been investigated.

Machine learning, Neural networks, Spectral analysis
• K. Pöpping, “Das Betriebs- und Alterungsverhalten biologisch schnell abbaubarer Hydrauliköle" Dissertation, April, 2012. G. E. Newell, “Oil analysis cost‐effective machine condition monitoring technique,” Ind. Lubr. Tribol., vol. 51, no. 3, pp. 119–124, Jun. 1999. A. K. S. Jardine, D. Lin, and D. Banjevic, “A review on machinery diagnostics and prognostics implementing condition-based maintenance,” Mech. Syst. Signal Process., vol. 20, no. 7, pp. 1483–1510, Oct. 2006. A. D. Stuart, S. M. Trotman, K. J. Doolan, and P. M. Fredericks, “Spectroscopic measurement of used lubricating oil quality,” Appl. Spectrosc., vol. 43, no. 1, pp. 55–60, January 1989. S. Paul, W. Legner, A. Hackner, V. Baumbach, and G. Müller, “Multi-parameter monitoring System für hydraulische Flüssigkeiten in Offshore-Windkraftgetrieben,” Tech. Mess., vol. 78, no. 5, pp. 260–267, 2011. A. Agoston, C. Oetsch, J. Zhuravleva, and B. Jakoby, “An IR-absorption sensor system for the determination of engine oil deterioration,” in Proceedings of IEEE Sensors, 2004., pp. 463–466. “CRISP-DM: Ein Standard-Prozess-Modell für Data Mining – Statistik Dresden.” [Online]. Available: https://statistik-dresden.de/archives/1128. [Accessed: 06-Apr-2019]. R. Wirth and J. Hipp, “CRISP-DM: Towards a Standard Process Model for Data Mining.” W. H. Press, S. A. Teukolsky, W. T. Vetterling, and B. P. Flannery, “Chapter 14. Statistical Description of Data. 14.9 Savitzky-Golay Smoothing Filters,” pp. 766–772, 2007. A. L. Samuel,“Some studies in machine learning using the game of Checkers,” IBM J. Res. Dev., pp. 71--105, 1959. T. Mitchell, Machine Learning. .McGraw-Hill Education Ltd., 1997 U. Fayyad, G. Piatetsky-Shapiro, and P. Smyth, “From Data Mining to Knowledge Discovery in Databases,” AI Mag., vol. 17, no. 3, pp. 37–37, March 1996. S. S. Haykin and S. S. Haykin, Neural networks and learning machines. Prentice Hall/Pearson, 2009. P. Ramachandran, B. Zoph, and Q. V. Le, “Searching for Activation Functions,” October 2017.
Primary Language en Articles Author: Marco STANG Author: Martin BOHME Author: Eric SAX Publication Date: June 21, 2019
 Bibtex @research article { epstem581184, journal = {The Eurasia Proceedings of Science Technology Engineering and Mathematics}, issn = {}, eissn = {2602-3199}, address = {ISRES Publishing}, year = {2019}, volume = {}, pages = {1 - 13}, doi = {}, title = {Applied Machine Learning: Reconstruction of Spectral Data for the Classification of Oil-Quality Levels}, key = {cite}, author = {STANG, Marco and BOHME, Martin and SAX, Eric} } APA STANG, M , BOHME, M , SAX, E . (2019). Applied Machine Learning: Reconstruction of Spectral Data for the Classification of Oil-Quality Levels. The Eurasia Proceedings of Science Technology Engineering and Mathematics, (5), 1-13. Retrieved from http://dergipark.org.tr/epstem/issue/46264/581184 MLA STANG, M , BOHME, M , SAX, E . "Applied Machine Learning: Reconstruction of Spectral Data for the Classification of Oil-Quality Levels". The Eurasia Proceedings of Science Technology Engineering and Mathematics (2019): 1-13 Chicago STANG, M , BOHME, M , SAX, E . "Applied Machine Learning: Reconstruction of Spectral Data for the Classification of Oil-Quality Levels". The Eurasia Proceedings of Science Technology Engineering and Mathematics (2019): 1-13 RIS TY - JOUR T1 - Applied Machine Learning: Reconstruction of Spectral Data for the Classification of Oil-Quality Levels AU - Marco STANG , Martin BOHME , Eric SAX Y1 - 2019 PY - 2019 N1 - DO - T2 - The Eurasia Proceedings of Science Technology Engineering and Mathematics JF - Journal JO - JOR SP - 1 EP - 13 VL - IS - 5 SN - -2602-3199 M3 - UR - Y2 - 2019 ER - EndNote %0 The Eurasia Proceedings of Science Technology Engineering and Mathematics Applied Machine Learning: Reconstruction of Spectral Data for the Classification of Oil-Quality Levels %A Marco STANG , Martin BOHME , Eric SAX %T Applied Machine Learning: Reconstruction of Spectral Data for the Classification of Oil-Quality Levels %D 2019 %J The Eurasia Proceedings of Science Technology Engineering and Mathematics %P -2602-3199 %V %N 5 %R %U ISNAD STANG, Marco , BOHME, Martin , SAX, Eric . "Applied Machine Learning: Reconstruction of Spectral Data for the Classification of Oil-Quality Levels". The Eurasia Proceedings of Science Technology Engineering and Mathematics / 5 (June 2019): 1-13. AMA STANG M , BOHME M , SAX E . Applied Machine Learning: Reconstruction of Spectral Data for the Classification of Oil-Quality Levels. EPSTEM. 2019; (5): 1-13. Vancouver STANG M , BOHME M , SAX E . Applied Machine Learning: Reconstruction of Spectral Data for the Classification of Oil-Quality Levels. The Eurasia Proceedings of Science Technology Engineering and Mathematics. 2019; (5): 13-1.