Load-Haul Cycle Segmentation with Hidden Semi-Markov Models

Precise tracking of haul truck activities in mining aids automation and data analysis and allows for accurate material tracking. Fleet Management Systems (FMS) record information about haul truck activities, however, today’s mines frequently employ legacy systems with limited sensing leading to poor data quality. In this paper we present a novel method for load-haul cycle segmentation using vehicle telemetry data to determine precise load and dump times. Central to this is a Hidden Semi-Markov Model (HSMM) which infers a truck’s state from discrete observations generated from GPS positional data. The HSMM is trained unsupervised from historic data. Evaluation of segmented state estimates made from real-world data showed over 98% of loads and 91% of dumps were correctly identified, demonstrating the effectiveness of the proposed method.