A robust ensemble surrogate model and its application in the in situ data modelling and analysis of a combine harvester

Abstract

In this article, a robust ensemble model is proposed based on extended adaptive hybrid functions and fuzzy clustering. In the outlier detection stage, each sample is assigned memberships to judge whether it is an outlier or not, where the memberships are determined based on the responses of the ensemble surrogate model of each cluster. Then, the detected outliers are removed from the initial training samples, and the final prediction model is constructed based on the remaining normal samples. The results of numerical problems and the in-situ dataset from a combine harvester show that the proposed model can provide accurate detection results for outliers and accurate prediction results for new points. The sensitivity analysis based on the proposed robust ensemble model indicates that the angle of guide plate, the open rate of cleaning fan, and the height of header have a greater effect on the cleaning loss of combine harvester.

KEYWORDS:

• Robust ensemble surrogate model
• combine harvester
• in situ data
• fuzzy clustering

Disclosure statement

No potential conflict of interest was reported by the authors.

Data availability statement

The data that support the findings of this study are available from the corresponding author upon reasonable request.

Additional information

Funding

This work is supported by the Jiangsu Province and Education Ministry Co-Sponsored Synergistic Innovation Center of Modern Agricultural Equipment [grant number XTCX2014]; National Natural Science Foundation of China [52205251]; the Natural Science Foundation of Jiangsu Province [grant number BK20210777]; the Project funded by China Postdoctoral Science Foundation [grant number 2022M711388]; Jiangsu Agricultural Science and Technology Innovation Fund [grant number CX(21)2042]; Jiangsu Graduate Research and Practice Innovation Program Project [grant number KYCX22_3678].