Personal information

Emails & domains

Verified email addresses

Websites & social links

Google 学术主页

Other IDs

Scopus Author ID: 58961681200
Scopus Author ID: 57203831510

Keywords

Deep Learning, Machine Learning, Fault Diagnosis

Countries

China

Activities

Collapse all

Education and qualifications (3)

Soochow University: Suzhou, CN

2023-09-01 to 2027-06-30 | PHD
Education
Source: Self-asserted source
Yan Zhang

Jiangsu Normal University: Xuzhou, CN

2020-09-01 to 2023-07-01
Education
Source: Self-asserted source
Yan Zhang

North China Institute of Science and Technology: Beijing, CN

2015-09-01 to 2019-07-01
Education
Source: Self-asserted source
Yan Zhang

Works (5)

Adaptive feature consolidation residual network for exemplar-free continuous diagnosis of rotating machinery with fault-type increments

Advanced Engineering Informatics
2024 | Journal article
DOI: 10.1016/j.aei.2024.102715
EID:

2-s2.0-85199085498

Part of ISSN: 14740346
Contributors: Zhang, Y.; Shen, C.; Zhong, X.; Chen, K.; Huang, W.; Zhu, Z.
Source: Self-asserted source
Yan Zhang via Scopus - Elsevier

Deep adaptive sparse residual networks: A lifelong learning framework for rotating machinery fault diagnosis with domain increments

Knowledge-Based Systems
2024-06 | Journal article
DOI: 10.1016/j.knosys.2024.111679
Part of ISSN: 0950-7051
Contributors: Yan Zhang; Changqing Shen; Juanjuan Shi; Chuan Li; Xinhai Lin; Zhongkui Zhu; Dong Wang
Source: Self-asserted source
Yan Zhang
grade
Preferred source (of 2)‎

A novel hierarchical hyper-parameter search algorithm based on greedy strategy for wind turbine fault diagnosis

Expert Systems with Applications
2022-09 | Journal article
DOI: 10.1016/j.eswa.2022.117473
Part of ISSN: 0957-4174
Source: Self-asserted source
Yan Zhang

A novel wind turbine fault diagnosis based on deep transfer learning of improved residual network and multi-target data

Measurement Science and Technology
2022-06-06 | Journal article | Writing - original draft
DOI: 10.1088/1361-6501/ac7036
Part of ISSN: 0957-0233
Part of ISSN: 1361-6501
Contributors: Yan Zhang
Source: Self-asserted source
Yan Zhang

A novel wind turbine fault diagnosis method based on compressed sensing and DTL-CNN

Renewable Energy
2022-05-24 | Journal article
DOI: 10.1016/j.renene.2022.05.085
Part of ISSN: 0960-1481
Source: Self-asserted source
Yan Zhang
grade
Preferred source (of 2)‎
How many people are using ORCID?