A novel unsupervised anomaly detection method for rotating machinery based on memory augmented temporal convolutional autoencoder-Reference-Cited by-同舟云学术

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A novel unsupervised anomaly detection method for rotating machinery based on memory augmented temporal convolutional autoencoder

Published:2023-08 Issue: Volume:123 Page:106312
ISSN:0952-1976
Container-title:Engineering Applications of Artificial Intelligence
language:en
Short-container-title:Engineering Applications of Artificial Intelligence

Author:

Li Wanxiang^ORCID,Shang Zhiwu^ORCID,Zhang Jie,Gao Maosheng,Qian Shiqi^ORCID

Funder

National Natural Science Foundation of China

Natural Science Foundation of Tianjin City

Civil Aviation Administration of China

Publisher

Elsevier BV

Subject

Electrical and Electronic Engineering,Artificial Intelligence,Control and Systems Engineering

Reference52 articles.

1. Fault detection and diagnosis with a novel source-aware autoencoder and deep residual neural network;Amini;Neurocomputing,2022

2. Implicit supervision for fault detection and segmentation of emerging fault types with deep variational autoencoders;Arias;Neurocomputing,2021

3. Anomaly monitoring improves remaining useful life estimation of industrial machinery;Aydemir;J. Manuf. Syst.,2020

4. An empirical evaluation of generic convolutional and recurrent networks for sequence modeling;Bai,2018

5. Convolutional neural network-based deep transfer learning for fault detection of gas turbine combustion chambers;Bai;Appl. Energy,2021

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3. A survey on fault diagnosis of rotating machinery based on machine learning;Measurement Science and Technology;2024-07-29

4. Dynamic normalization supervised contrastive network with multiscale compound attention mechanism for gearbox imbalanced fault diagnosis;Engineering Applications of Artificial Intelligence;2024-07

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