Learning from Class-imbalanced Data with a Model-Agnostic Framework for Machine Intelligent Diagnosis-Reference-Cited by-同舟云学术

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Learning from Class-imbalanced Data with a Model-Agnostic Framework for Machine Intelligent Diagnosis

Published:2021-12 Issue: Volume:216 Page:107934
ISSN:0951-8320
Container-title:Reliability Engineering & System Safety
language:en
Short-container-title:Reliability Engineering & System Safety

Author:

Wu Jingyao,Zhao Zhibin,Sun Chuang,Yan Ruqiang,Chen Xuefeng

Funder

National Development and Reform Commission

Shaanxi Province Department of Science and Technology

National Natural Science Foundation of China

Ministry of Science and Technology of the People's Republic of China

Chinese Aeronautical Establishment Aeronautical Science Foundation

Publisher

Elsevier BV

Subject

Industrial and Manufacturing Engineering,Safety, Risk, Reliability and Quality

Reference59 articles.

1. Weighted cyclic harmonic-to-noise ratio for rolling element bearing fault diagnosis;Mo;IEEE Trans Instrum Meas,2020

2. Non-stationary vibration feature extraction method based on sparse decomposition and order tracking for gearbox fault diagnosis;Li;Meas J Int Meas Confed,2018

3. A novel feature enhancement method based on improved constraint model of online dictionary learning;Wang;IEEE Access,2019

4. Deep decoupling convolutional neural network for intelligent compound fault diagnosis;Huang;IEEE Access,2019

5. A novel bearing intelligent fault diagnosis framework under time-varying working conditions using recurrent neural network;An;ISA Trans,2020

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4. Genetically optimised SMOTE-based adversarial discriminative domain adaptation for rotor fault diagnosis at variable operating conditions;Measurement Science and Technology;2024-07-11

5. Knowledge Distillation-Guided Cost-Sensitive Ensemble Learning Framework for Imbalanced Fault Diagnosis;IEEE Internet of Things Journal;2024-07-01

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