Real-Time Recognition of Loading Cycles’ Process Based on Electric Mining Shovel Monitoring

Abstract

An automatic recognition algorithm based on the feature extraction of working parameters to recognize each state in the loading cycle process of an electric mining shovel was proposed. The working parameters were collected using the electric shovel’s online monitoring system. The swing angle of the shovel boom and motor operating signals were used as key recognition objects; the waveform features of each stage were extracted as recognition marks in the loading cycle of an electric shovel using the time domain characteristic analysis method. The algorithm was developed to recognize the loading cycles in real time. Moreover, the dynamic time warping (DTW) algorithm was used to detect and classify the preliminary recognition results by optimizing its distance threshold parameters, reducing the error rate of the model. The method was validated by comparing synchronous video-recordings with the results of the algorithm. Results showed that the proposed recognition method of the shovel loading cycle process exhibited real-time performance and high accuracy in understanding the different work tasks, providing effective data support for mining and the analysis of shovel working parameters, helping to improve the energy efficiency of electric mining shovel.