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Machine-specific approach for automatic classification of cutting process efficiency

: Walther, Christian; Beneke, F.; Merbach, L.; Siebald, H.; Hensel, O.; Huster, J.

Volltext urn:nbn:de:0011-n-3701417 (1.1 MByte PDF)
MD5 Fingerprint: cb06f1d8992d313d8f131419cbd57394
Erstellt am: 15.12.2015

Niggemann, Oliver (Ed.); Beyerer, Jürgen (Ed.) ; Fraunhofer-Institut für Optronik, Systemtechnik und Bildauswertung -IOSB-, Karlsruhe:
ML4CPS 2015, 1st Conference on Machine Learning for Cyber Physical Systems and Industry 4.0. CD-ROM : Centrum Industrial IT, Lemgo, Germany, October 1st and 2nd, 2015
Lemgo, 2015
8 S.
Conference on Machine Learning for Cyber Physical Systems and Industry 4.0 (ML4CPS) <1, 2015, Lemgo>
Konferenzbeitrag, Elektronische Publikation
Fraunhofer IOSB ()
NAT; classification; condition monitoring; cutting process; machine; sensors; artificial neural network; vibration measurement

The identification of an inefficient cutting process e.g. in selfpropelled harvesters is a great challenge for automatic analysis. Machinespecific parameters of the process have to be examined to estimate the efficiency of the cutting process. As a contribution to that problem a simple method for indirect measurement of the efficiency is presented and described in this article. To establish a general algorithm, the vibration data of a harvesting machine were extracted. The data from two sensors were recorded while gathering whole crop silage and while standing still in operation mode. For every data stream, a spectral analysis and a feature extraction was performed. For the development of the algorithm, exploration techniques of Machine Learning were implemented. Artificial Neural Networks were optimized using subsets of the recorded data and then applied to the independent validation data to compute the efficiency of the cutting process. The established algorithm is able to identify the process efficiency without using additional machine-specific parameters. The validation results are presented as confusion matrices for each data set and the case-specific population of the generated Artificial Neural Networks. The described algorithm is able to automatically determine an inefficient and machine-specific cutting process as an additional information using vibration data only.