Automatic estimation of excavator actual and relative cycle times in loading operations

Abstract

This paper proposes a framework to automatically determine the productivity and operational effectiveness of an excavator. The method estimates the excavator's actual, theoretical, and relative cycle times in the loading operation. Firstly, a supervised learning algorithm is proposed to recognize excavator activities using motion data obtained from four inertial measurement units (IMUs) installed on different moving parts of the machine. The classification algorithm is offline trained using a dataset collected via an excavator operated by two operators with different levels of competence in different operating conditions. Then, an approach is presented to estimate the cycle time based on the sequence of activities detected using the trained classification model. Since operating conditions can significantly influence the cycle time, the actual cycle time cannot solely reveal the machine's performance. Hence, a benchmark or reference is required to analyze the actual cycle time. In the second step, the theoretical cycle time of an excavator is automatically estimated based on the operating conditions, such as swing angle and digging depth. Furthermore, two schemes are presented to estimate the swing angle and digging depth based on the recognized excavator activities. In the third step, the relative cycle time is obtained by dividing the theoretical cycle time by the actual cycle time. Finally, the results of the method are demonstrated by the implementation on two case studies which are operated by inexperienced and experienced operators. The obtained relative cycle time can effectively monitor the performance of an excavator in loading operations. The proposed method can be highly beneficial for worksite managers to monitor the performance of each machine in worksites.