Stall detection in hydraulic excavator operations using heuristics and machine learning: A case study

This work aims to develop a reliable algorithm for stall detection during excavator digging by analyzing key operational variables such as velocity and angular displacements from machine monitoring data. The work develops and validates a heuristic algorithm to detect stalling events and trains a support vector machine classification algorithm to distinguish between “normal” digging cycles and cycles with stalling. This work is a novel attempt at using a classification algorithm to categorize digging cycles into normal and those with stalling events based on machine monitoring data alone. The developed classification algorithm achieved a sensitivity of 100%, indicating it correctly identified all stalling cases, and a specificity of 99.0%, demonstrating its ability to accurately classify normal cases with minimal false positives. This study provides a foundation for stall detection and facilitates further study of the effect of stalling on excavator performance, improving operational reliability, and facilitating better excavator design.