Cargando…

A Hybrid SVD-Based Denoising and Self-Adaptive TMSST for High-Speed Train Axle Bearing Fault Detection

Fault detection of axle bearings is crucial to promote the safe, efficient, and reliable running of high-speed trains. In recent decades, time−frequency analysis (TFA) techniques have been widely used in mechanical equipment fault diagnoses. Time-reassigned multisynchrosqueezing transform (TMSST), a...

Descripción completa

Detalles Bibliográficos
Autores principales: Deng, Feiyue, Liu, Chao, Liu, Yongqiang, Hao, Rujiang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8469842/
https://www.ncbi.nlm.nih.gov/pubmed/34577232
http://dx.doi.org/10.3390/s21186025
_version_ 1784574041680510976
author Deng, Feiyue
Liu, Chao
Liu, Yongqiang
Hao, Rujiang
author_facet Deng, Feiyue
Liu, Chao
Liu, Yongqiang
Hao, Rujiang
author_sort Deng, Feiyue
collection PubMed
description Fault detection of axle bearings is crucial to promote the safe, efficient, and reliable running of high-speed trains. In recent decades, time−frequency analysis (TFA) techniques have been widely used in mechanical equipment fault diagnoses. Time-reassigned multisynchrosqueezing transform (TMSST), as a novel time−frequency representation (TFR) algorithm, is more suitable for dealing with strong frequency-varying signals. However, TMSST TFR results are subject to noise interference. It is difficult to extract the accurate time−frequency (TF) fault feature of the axle bearing under a complex working environment. In addition, determination of the TMSST algorithm parameters depends on the personnel’s subjective experience. Therefore, the TMSST result has a great randomicity and has the disadvantage of having a poor reliability. To address the above issues, a hybrid SVD-based denoising and self-adaptive TMSST is proposed for axle bearing fault detection in this paper. The main improvements of the proposed algorithm include the following two aspects: (1) An SVD-based denoising method using the maximum SV mean to determine the reasonable SV order is adopted to eliminate noise interference and to reserve useful fault impulse information. (2) A new evaluation metric, named time−frequency spectrum permutation entropy (TFS-PEn), is put forward for the quantitative evaluation of the performance of TFR for the TMSST, and then a water cycle algorithm (WCA)-based optimized TMSST can adaptively determine the optimal algorithm parameters. In both the simulation and experimental tests, the superiority and effectiveness of the proposed method is compared with the TMSST, short-time Fourier transform (STFT), MSST, wavelet transform (WT), and Hilbert-Huang transform (HHT) methods. The results show that the proposed algorithm has a better performance for extracting the weak fault features of axle bearing under a strong background noise environment.
format Online
Article
Text
id pubmed-8469842
institution National Center for Biotechnology Information
language English
publishDate 2021
publisher MDPI
record_format MEDLINE/PubMed
spelling pubmed-84698422021-09-27 A Hybrid SVD-Based Denoising and Self-Adaptive TMSST for High-Speed Train Axle Bearing Fault Detection Deng, Feiyue Liu, Chao Liu, Yongqiang Hao, Rujiang Sensors (Basel) Article Fault detection of axle bearings is crucial to promote the safe, efficient, and reliable running of high-speed trains. In recent decades, time−frequency analysis (TFA) techniques have been widely used in mechanical equipment fault diagnoses. Time-reassigned multisynchrosqueezing transform (TMSST), as a novel time−frequency representation (TFR) algorithm, is more suitable for dealing with strong frequency-varying signals. However, TMSST TFR results are subject to noise interference. It is difficult to extract the accurate time−frequency (TF) fault feature of the axle bearing under a complex working environment. In addition, determination of the TMSST algorithm parameters depends on the personnel’s subjective experience. Therefore, the TMSST result has a great randomicity and has the disadvantage of having a poor reliability. To address the above issues, a hybrid SVD-based denoising and self-adaptive TMSST is proposed for axle bearing fault detection in this paper. The main improvements of the proposed algorithm include the following two aspects: (1) An SVD-based denoising method using the maximum SV mean to determine the reasonable SV order is adopted to eliminate noise interference and to reserve useful fault impulse information. (2) A new evaluation metric, named time−frequency spectrum permutation entropy (TFS-PEn), is put forward for the quantitative evaluation of the performance of TFR for the TMSST, and then a water cycle algorithm (WCA)-based optimized TMSST can adaptively determine the optimal algorithm parameters. In both the simulation and experimental tests, the superiority and effectiveness of the proposed method is compared with the TMSST, short-time Fourier transform (STFT), MSST, wavelet transform (WT), and Hilbert-Huang transform (HHT) methods. The results show that the proposed algorithm has a better performance for extracting the weak fault features of axle bearing under a strong background noise environment. MDPI 2021-09-08 /pmc/articles/PMC8469842/ /pubmed/34577232 http://dx.doi.org/10.3390/s21186025 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Deng, Feiyue
Liu, Chao
Liu, Yongqiang
Hao, Rujiang
A Hybrid SVD-Based Denoising and Self-Adaptive TMSST for High-Speed Train Axle Bearing Fault Detection
title A Hybrid SVD-Based Denoising and Self-Adaptive TMSST for High-Speed Train Axle Bearing Fault Detection
title_full A Hybrid SVD-Based Denoising and Self-Adaptive TMSST for High-Speed Train Axle Bearing Fault Detection
title_fullStr A Hybrid SVD-Based Denoising and Self-Adaptive TMSST for High-Speed Train Axle Bearing Fault Detection
title_full_unstemmed A Hybrid SVD-Based Denoising and Self-Adaptive TMSST for High-Speed Train Axle Bearing Fault Detection
title_short A Hybrid SVD-Based Denoising and Self-Adaptive TMSST for High-Speed Train Axle Bearing Fault Detection
title_sort hybrid svd-based denoising and self-adaptive tmsst for high-speed train axle bearing fault detection
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8469842/
https://www.ncbi.nlm.nih.gov/pubmed/34577232
http://dx.doi.org/10.3390/s21186025
work_keys_str_mv AT dengfeiyue ahybridsvdbaseddenoisingandselfadaptivetmsstforhighspeedtrainaxlebearingfaultdetection
AT liuchao ahybridsvdbaseddenoisingandselfadaptivetmsstforhighspeedtrainaxlebearingfaultdetection
AT liuyongqiang ahybridsvdbaseddenoisingandselfadaptivetmsstforhighspeedtrainaxlebearingfaultdetection
AT haorujiang ahybridsvdbaseddenoisingandselfadaptivetmsstforhighspeedtrainaxlebearingfaultdetection
AT dengfeiyue hybridsvdbaseddenoisingandselfadaptivetmsstforhighspeedtrainaxlebearingfaultdetection
AT liuchao hybridsvdbaseddenoisingandselfadaptivetmsstforhighspeedtrainaxlebearingfaultdetection
AT liuyongqiang hybridsvdbaseddenoisingandselfadaptivetmsstforhighspeedtrainaxlebearingfaultdetection
AT haorujiang hybridsvdbaseddenoisingandselfadaptivetmsstforhighspeedtrainaxlebearingfaultdetection