Interpretable Fault Detection Approach With Deep Neural Networks to Industrial Applications-Reference-Cited by-同舟云学术

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Interpretable Fault Detection Approach With Deep Neural Networks to Industrial Applications

Published:2023-05-10 Issue: Volume: Page:
ISSN:
Container-title:2023 International Conference on Control, Automation and Diagnosis (ICCAD)
language:
Short-container-title:

Author:

Kakavandi Fatemeh¹,Han Peihua²,de Reus Roger³,Larsen Peter Gorm¹,Zhang Houxiang²

Affiliation:

1. Aarhus University,Department of Electrical and Computer Engineering,Aarhus,Denmark

2. Norwegian University of Science and Technology,Department of Ocean Operations and Civil Engineering,Alesund,Norway

3. Device Manufacturing Development Department 2540,Hillerod,Denmark

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10151847/10152298/10152435.pdf?arnumber=10152435

Reference21 articles.

1. Learning Deep Features for Discriminative Localization

2. Time series classification from scratch with deep neural networks: A strong baseline

3. Fast and interpretable classification of small X-ray diffraction datasets using data augmentation and deep neural networks

4. ConvTimeNet: A Pre-trained Deep Convolutional Neural Network for Time Series Classification

5. Online Fault Detection in Autonomous Ferries: Using Fault-type Independent Spectral Anomaly Detection

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