An Uncertainty-Adaptive Robust Graph-Based Belief Rule Base for Interpretable Four-State Fault Diagnosis of Industrial Robots
Year: 2026
Type: article
Abstract: Industrial robot severity diagnosis must combine evidence from coupled subsystems while remaining stable under fluctuating measurements. We develop an uncertainty-adaptive robust graph-based belief rule base (RG-BRB) that maps monitored attributes to transmission, servo-control, and motion-execution nodes. A row-stochastic graph propagates node evidence, while local fluctuation adjusts antecedent widths and attribute reliability before rule activation. Normalized evidential reasoning yields a belief distribution over normal, slight-degradation, severe-degradation, and failure-related states. An interpretability-constrained adaptive covariance search estimates graph and rule parameters under ordering and normalization constraints. The evaluation uses one record of 2000 observations, with a fixed 600-observation test partition for the primary comparison. Across 10 random-seed runs on that partition, RG-BRB achieved 91.55% mean Accuracy and 0.9115 mean Macro-F1. Condition-shift, noise, calibration, and ablation analyses on the same record show how subsystem-constrained propagation and reliability-aware matching contribute to robust four-state diagnosis.
Source: Mathematics
DOI: 10.3390/math14214097
Authors: Minyang Wang, Bangcheng Zhang, Zhi Gao
Language: English
Subfield: Control and Systems Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $3,137
OpenAlex ID: W7225660496
FWCI:
Cites:
Cited by:
Related to: