Research and Development and Application of Loader Tipping Warning Technology

With the widespread application of loaders in engineering construction and mining operations, the safety of loaders has received increasing attention. Lateral rollover accidents are one of the important issues affecting the safety of loader operations, which may cause casualties and equipment damage in severe cases. To this end, this paper proposes a loader lateral rollover warning technology based on multi-sensor data fusion. Through real-time monitoring of the loader's tilt angle, speed, center of gravity position and other information, combined with mathematical models to predict the rollover risk, and then provide a warning signal. The experimental results show that the technology has high warning accuracy and strong practical application value.

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Publication Details

Published
2025-03-14
DOI
https://doi.org/10.1109/itoec63606.2025.10968157
Primary Topic
Machine Fault Diagnosis Techniques
Type
article
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article

Research and Development and Application of Loader Tipping Warning Technology

Lingling Ning, Ranran Liu
Machine Fault Diagnosis Techniques
article

Research and Development and Application of Loader Tipping Warning Technology

Lingling Ning, Ranran Liu
article en

Abstract

With the widespread application of loaders in engineering construction and mining operations, the safety of loaders has received increasing attention. Lateral rollover accidents are one of the important issues affecting the safety of loader operations, which may cause casualties and equipment damage in severe cases. To this end, this paper proposes a loader lateral rollover warning technology based on multi-sensor data fusion. Through real-time monitoring of the loader's tilt angle, speed, center of gravity position and other information, combined with mathematical models to predict the rollover risk, and then provide a warning signal. The experimental results show that the technology has high warning accuracy and strong practical application value.

Shandong University of Technology (CN)
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Openalex Percentile: Top 91%
Machine Fault Diagnosis Techniques
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