A diagnosis framework based on domain adaptation for bearing fault diagnosis across diverse domains-Reference-Cited by-同舟云学术

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A diagnosis framework based on domain adaptation for bearing fault diagnosis across diverse domains

Published:2020-04 Issue: Volume:99 Page:465-478
ISSN:0019-0578
Container-title:ISA Transactions
language:en
Short-container-title:ISA Transactions

Author:

Ma Ping,Zhang Hongli,Fan Wenhui,Wang Cong

Funder

National Science Foundation of China

Outstanding Doctor Graduate Student Innovation Project ensemble, China

Graduate Student Innovation Project of Xinjiang Uygur Autonomous Region, China

Publisher

Elsevier BV

Subject

Applied Mathematics,Control and Systems Engineering,Electrical and Electronic Engineering,Computer Science Applications,Instrumentation

Reference29 articles.

1. A new family of model-based impulsive wavelets and their sparse representation for rolling bearing fault diagnosis;Qin;IEEE Trans Ind Electron,2018

2. Early fault diagnosis of bearing and stator faults of the single-phase induction motor using acoustic signals;Glowacz;Measurement,2018

3. Multi-feature entropy distance approach with vibration and acoustic emission signals for process feature recognition of rolling element bearing faults;Fei;Struct Health Monit,2018

4. A deviation based assessment methodology for multiple machine health patterns classification and fault detection;Jia;Mech Syst Signal Process,2018

5. A survey of fault diagnosis and fault-tolerant techniques—Part I: Fault diagnosis with model-based and signal-based approaches;Gao;IEEE Trans Ind Electron,2015

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1. TSMDA: intelligent fault diagnosis of rolling bearing with two stage multi-source domain adaptation;Measurement Science and Technology;2024-08-08

2. A Deep Transfer Learning Model for the Fault Diagnosis of Double Roller Bearing Using Scattergram Filter Bank 1;Vibration;2024-06-05

3. A New Bearing Fault Diagnosis Method Based on Deep Transfer Network and Supervised Joint Matching;IEEE Photonics Journal;2024-06

4. Domain generalization for cross-domain fault diagnosis: An application-oriented perspective and a benchmark study;Reliability Engineering & System Safety;2024-05

5. Towards multi-scene learning: A novel cross-domain adaptation model based on sparse filter for traction motor bearing fault diagnosis in high-speed EMU;Advanced Engineering Informatics;2024-04

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