Abstract
In view of the inconvenient installation and high cost of the current multi-sensor data prediction methods for predicting loader working resistance, this study proposes a method oriented towards predicting loader working resistance in environments with fewer sensors. First, building on previous research (Wu et al., 2023), non-essential sensor features are removed by a maximum information coefficient (MIC)method that incorporates expert experience. Second, the Optuna automation framework is embedded to realize the training and testing of the proposed method and compare its prediction performance with other popular methods. Finally, in order to verify its generalization performance, it is validated using loader operation data under different working conditions. The results of this study demonstrate that the proposed method effectively and accurately characterizes the work resistance of loaders under operating conditions. With short testing times and excellent generalization performance, the method proves highly applicable and valuable.
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