Processes, Vol. 13, Pages 2762: Real-Time Joint Fault Detection and Diagnosis of Hexapod Robot Based on Improved Random Forest

Processes doi: 10.3390/pr13092762

Authors:
Qilei Fang
Yifan Men
Kai Zhang
Man Yu
Yin Liu

In the field of robotic fault detection, although the random forest (RF) algorithm is widely adopted, its limited accuracy remains a critical constraint in practical engineering applications. To address this technical challenge, this study proposes a Two-Stages Random Forest (TSRF) algorithm. This approach constructs a hierarchical architecture with a dynamic adaptive weighting strategy, where the class probability vectors generated in the 1st-stage serve as meta-features for the 2nd-stage classifier. Such hierarchical optimization enables the model to precisely identify fault-sensitive features, effectively overcoming the performance limitations of conventional single-model frameworks. To validate the proposed approach, we conducted comparative experiments using a multidimensional kinematic feature dataset from hexapod robot joint fault detection. Benchmark models included geometry-feature-based RF and physics-informed RF as established baselines. Experimental results demonstrate that TSRF achieves a classification accuracy of 99.7% on the test set, representing an 18.8% improvement over standard RF. This significant advancement provides a novel methodological framework for intelligent fault diagnosis in complex electromechanical systems.

Source link

Qilei Fang www.mdpi.com