Estimation of Axle Torque for an Agricultural Tractor Using an Artificial Neural Network

Abstract

The objective of this study was to develop a model to estimate the axle torque (AT) of a tractor using an artificial neural network (ANN) based on a relatively low-cost sensor. ANN has proven to be useful in the case of nonlinear analysis, and it can be applied to consider nonlinear variables such as soil characteristics, unlike studies that only consider tractor major parameters, thus model performance and its implementation can be extended to a wider range. In this study, ANN-based models were compared with multiple linear regression (MLR)-based models for performance verification. The main input data were tractor engine parameters, major tractor parameters, and soil physical properties. Data of soil physical properties (i.e., soil moisture content and cone index) and major tractor parameters (i.e., engine torque, engine speed, specific fuel consumption, travel speed, tillage depth, and slip ratio) were collected during a tractor field experiment in four Korean paddy fields. The collected soil physical properties and major tractor parameter data were used to estimate the AT of the tractor by the MLR- and ANN-based models: 250 data points were used for developing and training the model were used, the 50 remaining data points were used to test the model estimation. The AT estimated with the developed MLR- and ANN-based models showed agreement with actual measured AT, with the R2 value ranging from 0.825 to 0.851 and from 0.857 to 0.904, respectively. These results suggest that the developed models are reliable in estimating tractor AT, while the ANN-based model showed better performance than the MLR-based model. This study can provide useful results as a simple method using ANNs based on relatively inexpensive sensors that can replace the existing complex tractor AT measurement method is emphasized.

Evaluation of Artificial Neural Network to Model Performance Attributes of a Mechanization Unit (Tractor-Chisel Plow) under Different Working Variables

“…Overall, our ANN-based models had R 2 in the range of 0.384 to 0.777 for the investigated attributes. These results were found to be less satisfactory to the main results of previous studies using ANNs in tillage research: draft force of a moldboard plow (R 2 = 0.9996) [81], fuel consumption of a moldboard plow (R 2 = 0.9996) [81], draft force of a chisel cultivator (correlation coefficient = 0.9445) [24], and tractor axle torque estimation (R 2 value ranging from 0.857 to 0.904) [82]. The variations in correlation values may be because our data were acquired from different field experiments.…”

“…The variations in correlation values may be because our data were acquired from different field experiments. Additionally, the variations are assumed to be due to the low input dimensions and high model complexity of the prediction model [82], which stated that higher input dimensions in an ANN model could make the model more fit to training data. As a result, the generalization performance of the ANN model reflected poor behavior.…”

Prediction of Higher Education Cost and Analysis of Sharing Ability Based on Artificial Neural Network

“…W N ij is the weight of the network edge formed from the i-th hidden node of layer N to the j-th node of layer n + 1, O N i (t) is the output of the i-th hidden node of layer N, and θ j (t) can be regarded as a constant term of linear combination [11]. e definition of the total prediction error at the output node of the t-th iteration is…”

The Influence of Soil Physical Properties on the Load Factor for Agricultural Tractors in Different Paddy Fields

“…The performance of the estimative model based on regression analysis was evaluated by referring to previous studies. For the assessment of model performance, four statistical metrics were chosen: the coefficient of determination (R 2 ), mean absolute percentage error (MAPE), root mean square error (RMSE), and relative deviation (RD) [27]. Each of these model performance metrics was calculated using Equations ( 7)- (10), based on the actual and estimated engine loads.…”