Adaptive Fault Components Extraction by Using an Optimized Weights Spectrum Based Index for Machinery Fault Diagnosis-Reference-Cited by-同舟云学术

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Adaptive Fault Components Extraction by Using an Optimized Weights Spectrum Based Index for Machinery Fault Diagnosis

Published:2024-01 Issue:1 Volume:71 Page:985-995
ISSN:0278-0046
Container-title:IEEE Transactions on Industrial Electronics
language:
Short-container-title:IEEE Trans. Ind. Electron.

Author:

Hou Bingchang¹^ORCID,Wang Dong¹^ORCID,Peng Zhike²^ORCID,Tsui Kwok-Leung³^ORCID

Affiliation:

1. Department of Industrial Engineering and Management, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, China

2. School of Mechanical Engineering, Ningxia University, Ningxia, China

3. Grado Department of Industrial and Systems Engineering, Virginia Polytechnic Institute and State University, Blacksburg, VA, USA

Funder

National Natural Science Foundation of China

Shanghai Jiao Tong University

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Control and Systems Engineering

Link

http://xplorestaging.ieee.org/ielx7/41/10184152/10043805.pdf?arnumber=10043805

Reference46 articles.

1. A review on the application of blind deconvolution in machinery fault diagnosis

2. Application of an improved kurtogram method for fault diagnosis of rolling element bearings

3. Fast computation of the kurtogram for the detection of transient faults

4. A smoothness index-guided approach to wavelet parameter selection in signal de-noising and fault detection

5. Variational Mode Decomposition

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1. Robust optimized weights spectrum: Enhanced interpretable fault feature extraction method by solving frequency fluctuation problem;Mechanical Systems and Signal Processing;2025-01

2. A two-stage importance-aware subgraph convolutional network based on multi-source sensors for cross-domain fault diagnosis;Neural Networks;2024-11

3. Heterogeneous graph representation-driven multiplex aggregation graph neural network for remaining useful life prediction of bearings;Mechanical Systems and Signal Processing;2024-11

4. A post-processing method called Fourier transform based on local maxima of autocorrelation function for extracting fault feature of bearings;Advanced Engineering Informatics;2024-10

5. An interpretable waveform segmentation model for bearing fault diagnosis;Advanced Engineering Informatics;2024-08

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