A Novel Method on Recognizing Drum Load of Elastic Tooth Drum Pepper Harvester Based on CEEMDAN-KPCA-SVM

Xinyu Zhang, Xinyan Qin, Jin Lei (), Zhiyuan Zhai, Jianglong Zhang and Zhi Wang
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Xinyu Zhang: College of Mechanical and Electrical Engineering, Shihezi University, Shihezi 832003, China
Xinyan Qin: College of Mechanical and Electrical Engineering, Shihezi University, Shihezi 832003, China
Jin Lei: College of Mechanical and Electrical Engineering, Shihezi University, Shihezi 832003, China
Zhiyuan Zhai: College of Mechanical and Electrical Engineering, Shihezi University, Shihezi 832003, China
Jianglong Zhang: College of Mechanical and Electrical Engineering, Shihezi University, Shihezi 832003, China
Zhi Wang: College of Mechanical and Electrical Engineering, Shihezi University, Shihezi 832003, China

Agriculture, 2024, vol. 14, issue 7, 1-20

Abstract: The operational complexities of the elastic tooth drum pepper harvester (ETDPH), characterized by variable drum loads that are challenging to recognize due to varying pepper densities, significantly impact pepper loss rates and mechanical damage. This study proposes a novel method integrating complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), kernel principal component analysis (KPCA), and a support vector machine (SVM) to enhance drum load recognition. The method consists of three principal steps: the initial experiments with ETDPHs to identify the critical factors affecting drum load and to formulate classification criteria; the development of a CEEMDAN-KPCA-SVM model for ETDPH drum load recognition, where drum spindle torque signals are processed by CEEMDAN for decomposition and reconstruction, followed by feature extraction and dimensionality reduction via KPCA to refine the model’s accuracy and training efficiency; and evaluation of the model’s performance on real datasets, highlighting the improvements brought by CEEMDAN and KPCA, as well as comparative analysis with other machine learning models. The results describe four load conditions—no load (mass of pepper intake (MOPI) = 0 kg/s), low load (0 < MOPI ≤ 0.658 kg/s), normal load (0.658 < MOPI ≤ 1.725 kg/s), and high load (MOPI > 1.725 kg/s)—with the CEEMDAN-KPCA-SVM model achieving 100% accuracy on both training and test sets, outperforming the standalone SVM by 6% and 12.5%, respectively. Additionally, it reduced the training time to 2.88 s, a 10.9% decrease, and reduced the prediction time to 0.0001 s, a 63.6% decrease. Comparative evaluations confirmed the superiority of the CEEMDAN-KPCA-SVM model over random forest (RF) and gradient boosting machine (GBM) in classification tasks. The synergistic application of CEEMDAN and KPCA significantly improved the accuracy and operational efficiency of the SVM model, providing valuable insights for load recognition and adaptive control of ETDPH drum parameters.

Keywords: ETDPH; load recognition; CEEMDAN; data-reduced dimension; SVM; RF; GBM (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2024
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