FGDAE: A new machinery anomaly detection method towards complex operating conditions

Yan, Shen; Shao, Haidong; Min, Zhishan; Peng, Jiangji; Cai, Baoping; Liu, Bin

doi:10.1016/j.ress.2023.109319

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FGDAE: A new machinery anomaly detection method towards complex operating conditions

Author(s): Shen Yan , Haidong Shao , Zhishan Min , Jiangji Peng , Baoping Cai , Bin Liu

Publication date Created: August 2023

Publication date (Print): August 2023

Journal: Reliability Engineering & System Safety

Publisher: Elsevier BV

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Highly-Accurate Machine Fault Diagnosis Using Deep Transfer Learning

Siyu Shao, Stephen McAleer, Ruqiang Yan … (2018)

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Remaining useful life estimation in prognostics using deep convolution neural networks

Xiang Li, Qian Ding, Jian-Qiao Sun (2018)

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A sparse auto-encoder-based deep neural network approach for induction motor faults classification

Siyu Shao, Rui Zhao, Wenjun Sun … (2016)

0 comments Cited 117 times – based on 0 reviews      Review now

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Haidong Shao: (View ORCID Profile)

Journal

Title: Reliability Engineering & System Safety

Abbreviated Title: Reliability Engineering & System Safety

Publisher: Elsevier BV

ISSN (Print): 09518320

Publication date Created: August 2023

Publication date (Print): August 2023

Volume: 236

Page: 109319

Article

DOI: 10.1016/j.ress.2023.109319

SO-VID: 145122ed-8cbc-4299-9714-b687d64e4f2e

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FGDAE: A new machinery anomaly detection method towards complex operating conditions

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Chinese Journal of Radiological Health

Most cited references 39

Highly-Accurate Machine Fault Diagnosis Using Deep Transfer Learning

Remaining useful life estimation in prognostics using deep convolution neural networks

A sparse auto-encoder-based deep neural network approach for induction motor faults classification

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