← Back to all news
arXiv Machine Learning By Mainak Mallick, Seung-Kyum Choi

When Labels Are Scarce: An Oscillatory State Space Model for Vibration Diagnosis

Read the original on arXiv Machine Learning →

The paper introduces DualRes, a compact oscillatory state‑space model designed for vibration‑based fault diagnosis when labeled data are scarce and computational resources are limited. DualRes integrates two spectral views of vibration and employs selective oscillatory memory to learn how long to retain temporal patterns, resulting in a lightweight encoder with only 39,528 parameters. Evaluations on six bearing datasets and a gearbox benchmark show that DualRes outperforms nine competing methods across most label budgets, achieving significant gains in macro‑F1, faster inference, and reduced storage requirements.

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.

Related stories

Hugging Face Trending Papers
Jun 23

Digital Twin-Driven Adaptive Sim-to-Real Alignment via Reinforcement Learning for Vibration-Based Bearing Health Monitoring Under Data Scarcity

Vibration-based health monitoring of rotating machinery requires reliable fault diagnosis under operational data constraints, yet condition assessment remains challenged by structural scarcity of fault events and heterogeneous sim-to-real gaps in digital twin-generated signals. Each fault type generates impulses with distinct periodicity, amplitude modulation, and spectral character, making feature-space discrepancies fundamentally heterogeneous across fault classes.

reinforcement-learningroboticsfine-tuningsafety
More like this →
arXiv Machine Learning
Jun 25

Digital Twin-Driven Adaptive Sim-to-Real Alignment via Reinforcement Learning for Vibration-Based Bearing Health Monitoring Under Data Scarcity

arXiv:2606. 24954v1 Announce Type: new Abstract: Vibration-based health monitoring of rotating machinery requires reliable fault diagnosis under operational data constraints, yet condition assessment remains challenged by structural scarcity of fault events and heterogeneous sim-to-real gaps in digital twin-generated signals.

By Jinghan Wang, Yanjun Chen, Wei Zhang, Wentao Wu, Tianchen Liu, Gaoliang Peng
reinforcement-learningroboticsfine-tuningsafety
More like this →
arXiv Machine Learning
Sep 10

Deep Learning Approach to Bearing and Induction Motor Fault Diagnosis via Data Fusion

The paper presents a deep learning framework that uses Convolutional Neural Networks to analyze accelerometer and microphone data for diagnosing bearing and induction motor faults. It further employs a Long Short-Term Memory network to fuse these sensor streams, demonstrating the advantages of data fusion. The authors advocate for multi‑model diagnosis and encourage the collection of diverse multi‑sensor datasets, such as acoustic and accelerometer recordings, for constant‑speed data collection.

By Mert Sehri, Merve Ertargin, Ozal Yildirim, Ahmet Orhan, Patrick Dumond
More like this →