Intelligent identification for working-cycle stages of excavator based on main pump pressure

Abstract An identification of the working stages of the excavator is used as the basis to realize a staged energy-saving control and reduce the fuel consumption. However, existing methods based on computer vision and multi-sensor information fusion technologies have limitations in practical applications. In this paper, an intelligent identification method for the working-cycle stages of an excavator is proposed based on the relationship between the working stages and the main pump pressure waveform. Three machine learning algorithms, a support vector machine (SVM), back propagation neural network (BPNN), and logistic regression (LR), were used to establish the intelligent recognition model. In addition, an intelligent calibration system was established to reduce the effects of human-induced factors and pressure fluctuations on the accuracy when using the original main pump pressure signals. The relationships among the identification accuracy, time window width, and signal sampling frequency were also studied. The results show that the library for support vector machine (LIBSVM) model outperforms the BPNN and LR, recording an accuracy of 94.64% when using the test set and an accuracy of 93.82% with an intelligent calibration system when using the original main pump pressure signals. The optimal time window width and signal sampling frequency are 0.5 s and 20 Hz, respectively. The proposed method is a practical approach to realize the real-time supervision of the working stage of an excavator, providing theoretical support for a staged energy-saving control.

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