arXiv Machine Learning By Evgeny Nikulchev, Dmitry Ilin

Neural Network Implementation of the Renormalization Group for Fault Diagnosis with Class Imbalance

Read the original on arXiv Machine Learning →

arXiv:2606. 18326v1 Announce Type: new Abstract: The application of machine learning models in practical tasks faces challenges such as class imbalance and multidimensional noise.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

arXiv Machine Learning
Jun 19

Evaluating deep learning models for fault diagnosis of a rotating machinery with epistemic and aleatoric uncertainty

arXiv:2412. 18980v2 Announce Type: replace Abstract: Uncertainty-aware deep learning (DL) models recently gained attention in fault diagnosis as a way to promote the reliable detection of faults when out-of-distribution (OOD) data arise from unseen faults (epistemic uncertainty) or the presence of noise (aleatoric uncertainty).

By Reza Jalayer, Masoud Jalayer, Andrea Mor, Carlotta Orsenigo, Carlo Vercellis
arXiv Machine Learning
Sep 22

Contrastive Siamese Representation Learning for Predictive Maintenance of Electrical Submersible Pumps

The paper introduces a fault‑diagnosis framework for electrical submersible pumps that uses Siamese contrastive representation learning to handle class imbalance and a prior‑corrected k‑nearest neighbor classifier for robust fault classification. It extracts discriminative vibration‑domain features, trains a Siamese network to cluster same‑fault samples, and applies a distance‑weighted KNN to mitigate imbalance. Validation with a Leave‑One‑ESP‑Out strategy shows consistent performance across unseen pump units, indicating potential for reliable predictive maintenance in offshore oil production.

By Seshu K. Damarla, Xiuli Zhu