Generating feature sets for fault diagnosis using denoising stacked auto-encoder-Reference-Cited by-同舟云学术

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Generating feature sets for fault diagnosis using denoising stacked auto-encoder

Published:2016-06 Issue: Volume: Page:
ISSN:
Container-title:2016 IEEE International Conference on Prognostics and Health Management (ICPHM)
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Author:

Thirukovalluru Raghuveer,Dixit Sonal,Sevakula Rahul K.,Verma Nishchal K.,Salour Al

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/7530388/7542814/07542865.pdf?arnumber=7542865

Cited by 48 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. Artificial Intelligence in Point-of-Care Biosensing: Challenges and Opportunities;Diagnostics;2024-05-25

2. A Multi-scale Attention-Based Transfer Model for Cross-bearing Fault Diagnosis;International Journal of Computational Intelligence Systems;2024-03-06

3. A Unified Label Noise-Tolerant Framework of Deep Learning-Based Fault Diagnosis via a Bounded Neural Network;IEEE Transactions on Instrumentation and Measurement;2024

4. Zero-Shot Generative AI for Rotating Machinery Fault Diagnosis: Synthesizing Highly Realistic Training Data via Cycle-Consistent Adversarial Networks;Applied Sciences;2023-11-17

5. Denoising magnetic resonance spectroscopy (MRS) data using stacked autoencoder for improving signal‐to‐noise ratio and speed of MRS;Medical Physics;2023-11-10

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