Hyperparameter Analysis of Wide-Kernel CNN Architectures in Industrial Fault Detection - An Exploratory Study-Reference-Cited by-同舟云学术

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Hyperparameter Analysis of Wide-Kernel CNN Architectures in Industrial Fault Detection - An Exploratory Study

Published:2023-10-09 Issue: Volume: Page:
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Container-title:2023 IEEE 10th International Conference on Data Science and Advanced Analytics (DSAA)
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Author:

Van Den Hoogen Jurgen¹,Hudson Dan²,Bloemheuvel Stefan¹,Atzmueller Martin³

Affiliation:

1. Tilburg University,Jheronimus Academy of Data Science,’s-Hertogenbosch,The Netherlands

2. Osnabrück University,Semantic Information Systems Group,Osnabrueck,Germany

3. Osnabrück University,Semantic Information Systems Group German Research Center for Artificial Intelligence (DFKI),Osnabrück,Germany

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10302431/10302461/10302625.pdf?arnumber=10302625

Reference11 articles.

1. CWRU dataset; case western reserve university bearing data center,0

2. One-dimensional convolutional auto-encoder-based feature learning for fault diagnosis of multivariate processes

3. Gearbox fault diagnosis data;pandya,2018

4. A New Deep Learning Model for Fault Diagnosis with Good Anti-Noise and Domain Adaptation Ability on Raw Vibration Signals

5. Classifying Multivariate Signals in Rolling Bearing Fault Detection Using Adaptive Wide-Kernel CNNs

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Data-Hungry Fault Detection Algorithms Can Try Transfer Learning for Starters;2024 IEEE 40th International Conference on Data Engineering Workshops (ICDEW);2024-05-13

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