Research on the Prediction of Insertion Resistance of Wheel Loader Based on PSO-LSTM

Abstract

Insertion resistance is the resistance caused by a pile to a wheel loader when the latter inserts into the pile. It is significant to clarify the insertion resistance to avoid wheel slippage, increase additional energy consumption, and protect the wheel loader during the insertion process. To address the problem that current methods cannot accurately obtain the insertion resistance magnitude and insertion resistance variation trend, we propose a composite model based on the particle swarm optimization (PSO) algorithm and the long short-term memory (LSTM) neural network. Firstly, the Pearson correlation coefficient method is used to test the parameters related to insertion resistance. Following this, the hyperparameters in the LSTM are optimized by PSO. Finally, different proportions of training sets are set in PSO-LSTM and compared with LSTM. The experimental data are selected from gravel sample groups and sand sample groups consisting of insertion depths of 600 mm, 800 mm, and 1000 mm. The results show that PSO-LSTM has higher prediction accuracy, better robustness, stability, and generalization ability compared with LSTM. In PSO-LSTM, when the proportion of the training set is 80%, the average relative errors are 2.28%, 1.57%, and 1.53% for the gravel sample group and 1.14%, 0.71%, and 0.60% for the sand sample group.

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“…Furthermore, some researchers have introduced intelligent optimization algorithms to enhance the performance of the proposed models, such as Particle Swarm Optimization (PSO) [37], the Grey Wolf Optimizer (GWO) [38], and the Cuckoo Search (CS) [39]. Experimental results indicate that the use of optimization algorithms can improve the predictive capability of the proposed models.…”