Geometric Attractor Monitoring: A Robust and Frugal Framework for Multi-modal Industrial Robotic Cycles

arXiv:2608.30804 · cs.LG · Submitted 2026-08-31 · Read on arXiv

cs.LG

Submitted: 2026-08-31

Updated: 2026-08-31

Comments: 17 pages, 6 figures. Accepted at ECML PKDD 2026

Code: https://github.com/mart1bb/geometric-attractor-monitoring

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Health Indicator — 15× in this paper
• Discrete Support Estimation — 7× in this paper
• Phase Space Reconstruction — 7× in this paper
• Calibration — 5× in this paper · explained in 7 episodes
• Predictive Maintenance — 5× in this paper · explained in 2 episodes
• Remaining Useful Life — 4× in this paper
• Benchmark — 3× in this paper · explained in 2 episodes
• Latent Space — 3× in this paper · explained in 7 episodes
• Over-smoothing — 3× in this paper · explained in 4 episodes
• Reconstruction Error — 3× in this paper · explained in 2 episodes
• Robustness — 3× in this paper · explained in 5 episodes
• Algorithmic Bias — 2× in this paper · explained in 1 episode
• Average Mutual Information — 2× in this paper
• Consistency — 2× in this paper · explained in 3 episodes
• Mean Squared Error — 2× in this paper
• Prognostics and Health Management — 2× in this paper
• Transformer — 2× in this paper · explained in 2 episodes
• Accuracy — 1× in this paper · explained in 1 episode
• Adam — 1× in this paper · explained in 2 episodes
• Aggregation — 1× in this paper · explained in 1 episode
• Bayesian Framework — 1× in this paper · explained in 3 episodes
• Computational overhead — 1× in this paper · explained in 2 episodes
• Continual Learning — 1× in this paper · explained in 2 episodes
• Contrastive Learning — 1× in this paper · explained in 5 episodes
• Curse of Dimensionality — 1× in this paper · explained in 6 episodes
• Data Fusion — 1× in this paper · explained in 3 episodes
• Failure Modes — 1× in this paper · explained in 3 episodes
• False Nearest Neighbor — 1× in this paper
• Fine-Tuning — 1× in this paper · explained in 5 episodes
• Gaussian Mixture Models — 1× in this paper
• Generalizability — 1× in this paper · explained in 6 episodes
• One-Class Support Vector Machines — 1× in this paper
• Representation Learning — 1× in this paper · explained in 2 episodes
• Sparsity — 1× in this paper · explained in 4 episodes
• Trace — 1× in this paper · explained in 2 episodes
• Transfer Learning — 1× in this paper · explained in 10 episodes
• Variational Autoencoder (VAE) — 1× in this paper · explained in 3 episodes

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