Machine-specific Approach for Automatic Classification of Cutting Process Efficiency

Abstract

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.