Fault Detection of High-Speed Train Wheelset Bearing Based on Impulse-Envelope Manifold

A novel fault detection method employing the impulse-envelope manifold is proposed in this paper which is based on the combination of convolution sparse representation (CSR) and Hilbert transform manifold learning. The impulses with different sparse characteristics are extracted by the CSR with diff...

Full description

Saved in:
Bibliographic Details
Main Authors: Zhe Zhuang, Jianming Ding, Andy C. Tan, Ying Shi, Jianhui Lin
Format: Article
Language:English
Published: Wiley 2017-01-01
Series:Shock and Vibration
Online Access:http://dx.doi.org/10.1155/2017/2104720
Tags: Add Tag
No Tags, Be the first to tag this record!
_version_ 1832568381564256256
author Zhe Zhuang
Jianming Ding
Andy C. Tan
Ying Shi
Jianhui Lin
author_facet Zhe Zhuang
Jianming Ding
Andy C. Tan
Ying Shi
Jianhui Lin
author_sort Zhe Zhuang
collection DOAJ
description A novel fault detection method employing the impulse-envelope manifold is proposed in this paper which is based on the combination of convolution sparse representation (CSR) and Hilbert transform manifold learning. The impulses with different sparse characteristics are extracted by the CSR with different penalty parameters. The impulse-envelope space is constructed through Hilbert transform on the extracted impulses. The manifold based on impulse-envelope space (impulse-envelope manifold) is executed to learn the low-dimensionality intrinsic envelope of vibration signals for fault detection. The analyzed results based on simulations, experimental tests, and practical applications show that (1) the impulse-envelope manifold with both isometric mapping (Isomap) and locally linear coordination (LLC) can be successfully used to extract the intrinsic envelope of the impulses where local tangent space analysis (LTSA) fails to perform and (2) the impulse-envelope manifold with Isomap outperforms those with LLC in terms of strengthening envelopes and the number of extracted harmonics. The proposed impulse-envelope manifold with Isomap is superior in extracting the intrinsic envelope, strengthening the amplitude of intrinsic envelope spectra, and enlarging the harmonic number of fault-characteristic frequency. The proposed technique is highly suitable for extracting intrinsic envelopes for bearing fault detection.
format Article
id doaj-art-b278eba4b8db44f5b7a9f7e3eb3b9f1a
institution Kabale University
issn 1070-9622
1875-9203
language English
publishDate 2017-01-01
publisher Wiley
record_format Article
series Shock and Vibration
spelling doaj-art-b278eba4b8db44f5b7a9f7e3eb3b9f1a2025-02-03T00:59:13ZengWileyShock and Vibration1070-96221875-92032017-01-01201710.1155/2017/21047202104720Fault Detection of High-Speed Train Wheelset Bearing Based on Impulse-Envelope ManifoldZhe Zhuang0Jianming Ding1Andy C. Tan2Ying Shi3Jianhui Lin4State Key Laboratory of Traction Power, Southwest Jiaotong University, Chengdu 610031, ChinaState Key Laboratory of Traction Power, Southwest Jiaotong University, Chengdu 610031, ChinaLKC Faculty of Engineering and Science, Universiti Tunku Abdul Rahman, 43000 Kajang, MalaysiaState Key Laboratory of Traction Power, Southwest Jiaotong University, Chengdu 610031, ChinaState Key Laboratory of Traction Power, Southwest Jiaotong University, Chengdu 610031, ChinaA novel fault detection method employing the impulse-envelope manifold is proposed in this paper which is based on the combination of convolution sparse representation (CSR) and Hilbert transform manifold learning. The impulses with different sparse characteristics are extracted by the CSR with different penalty parameters. The impulse-envelope space is constructed through Hilbert transform on the extracted impulses. The manifold based on impulse-envelope space (impulse-envelope manifold) is executed to learn the low-dimensionality intrinsic envelope of vibration signals for fault detection. The analyzed results based on simulations, experimental tests, and practical applications show that (1) the impulse-envelope manifold with both isometric mapping (Isomap) and locally linear coordination (LLC) can be successfully used to extract the intrinsic envelope of the impulses where local tangent space analysis (LTSA) fails to perform and (2) the impulse-envelope manifold with Isomap outperforms those with LLC in terms of strengthening envelopes and the number of extracted harmonics. The proposed impulse-envelope manifold with Isomap is superior in extracting the intrinsic envelope, strengthening the amplitude of intrinsic envelope spectra, and enlarging the harmonic number of fault-characteristic frequency. The proposed technique is highly suitable for extracting intrinsic envelopes for bearing fault detection.http://dx.doi.org/10.1155/2017/2104720
spellingShingle Zhe Zhuang
Jianming Ding
Andy C. Tan
Ying Shi
Jianhui Lin
Fault Detection of High-Speed Train Wheelset Bearing Based on Impulse-Envelope Manifold
Shock and Vibration
title Fault Detection of High-Speed Train Wheelset Bearing Based on Impulse-Envelope Manifold
title_full Fault Detection of High-Speed Train Wheelset Bearing Based on Impulse-Envelope Manifold
title_fullStr Fault Detection of High-Speed Train Wheelset Bearing Based on Impulse-Envelope Manifold
title_full_unstemmed Fault Detection of High-Speed Train Wheelset Bearing Based on Impulse-Envelope Manifold
title_short Fault Detection of High-Speed Train Wheelset Bearing Based on Impulse-Envelope Manifold
title_sort fault detection of high speed train wheelset bearing based on impulse envelope manifold
url http://dx.doi.org/10.1155/2017/2104720
work_keys_str_mv AT zhezhuang faultdetectionofhighspeedtrainwheelsetbearingbasedonimpulseenvelopemanifold
AT jianmingding faultdetectionofhighspeedtrainwheelsetbearingbasedonimpulseenvelopemanifold
AT andyctan faultdetectionofhighspeedtrainwheelsetbearingbasedonimpulseenvelopemanifold
AT yingshi faultdetectionofhighspeedtrainwheelsetbearingbasedonimpulseenvelopemanifold
AT jianhuilin faultdetectionofhighspeedtrainwheelsetbearingbasedonimpulseenvelopemanifold