A novel denoising strategy based on sparse modeling for rotating machinery fault detection under time-varying operating conditions-Reference-Cited by-同舟云学术

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A novel denoising strategy based on sparse modeling for rotating machinery fault detection under time-varying operating conditions

Published:2023-03 Issue: Volume:210 Page:112534
ISSN:0263-2241
Container-title:Measurement
language:en
Short-container-title:Measurement

Author:

Liu Zimin,Zhou Haoxuan,Wen Guangrui^ORCID,Lei Zihao^ORCID,Su Yu,Chen Xuefeng

Funder

National Key Research and Development Program of China

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

Publisher

Elsevier BV

Subject

Applied Mathematics,Electrical and Electronic Engineering,Condensed Matter Physics,Instrumentation

Reference55 articles.

1. Remaining useful life prediction of machinery under time-varying operating conditions based on a two-factor state-space model;Li;Reliability Engineering & System Safety,2019

2. Data-driven time-frequency analysis method based on variational mode decomposition and its application to gear fault diagnosis in variable operating conditions;Li;Mechanical Systems and Signal Processing,2019

3. Convolution enabled transformer via random contrastive regularization for rotating machinery diagnosis under time-varying operating conditions;Zhou;Mechanical Systems and Signal Processing,2022

4. Sound-aided vibration weak signal enhancement for bearing fault detection by using adaptive stochastic resonance;Lu;Journal of Sound and Vibration,2019

5. Periodicity-enhanced sparse representation for rolling bearing incipient fault detection;Yao;ISA transactions,2021

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1. Efficient online surface defect detection using multiple instance learning;Expert Systems with Applications;2024-10

2. A fault diagnosis method using improved prototypical network and weighting similarity-Manhattan distance with insufficient noisy data;Measurement;2024-02

3. Rotating Machinery Fault Diagnosis Under Time-Varying Speeds: A Review;IEEE Sensors Journal;2023-12-15

4. An enhanced cyclostationary method and its application on the incipient fault diagnosis of induction motors;Measurement;2023-11

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