Abstract

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.

Department(s)

Mining Engineering

Publication Status

Open Access

Keywords and Phrases

classification; data analysis; heuristics; Hydraulic excavators; machine learning; support vector machine

International Standard Serial Number (ISSN)

2572-6676; 2572-6668

Document Type

Article - Journal

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2026 SAGE Publications; Australasian Institute of Mining and Metallurgy; Institute of Materials, Minerals and Mining (UK), All rights reserved.

Creative Commons Licensing

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.

Publication Date

01 Jan 2026

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