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Identification of modal parameters from noisy transient response signals

Dan He, Xiufeng Wang, Michael Friswell, Jing Lin

Structural Control and Health Monitoring, Start page: e2019

Swansea University Author: Michael Friswell

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DOI (Published version): 10.1002/stc.2019

Abstract

In the process of impact testing of large-scale mechanical equipment, the measured forced response signals are often polluted by strong background noise. The forced response signal has a low signal-to-noise ratio, and this makes it difficult to accurately estimate the modal parameters. To solve this...

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Published in: Structural Control and Health Monitoring
ISSN: 15452255
Published: 2017
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URI: https://cronfa.swan.ac.uk/Record/cronfa33695
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first_indexed 2017-05-17T18:58:54Z
last_indexed 2018-02-09T05:23:04Z
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spelling 2017-07-31T14:43:15.3275807 v2 33695 2017-05-17 Identification of modal parameters from noisy transient response signals 5894777b8f9c6e64bde3568d68078d40 Michael Friswell Michael Friswell true false 2017-05-17 FGSEN In the process of impact testing of large-scale mechanical equipment, the measured forced response signals are often polluted by strong background noise. The forced response signal has a low signal-to-noise ratio, and this makes it difficult to accurately estimate the modal parameters. To solve this problem, the mean averaging of repeatedly measured frequency response function estimates is often employed in practical applications. However, a large number of impact tests are not practical for the modal testing of large-scale mechanical equipment. The primary objective of this paper is to reduce the averaging operation and improve the accuracy of the modal identification by using a noise removal technique. A hybrid denoising method is proposed by combining the Wiener and improved minimum mean-square-error short-time spectral amplitude estimators. The proposed method can effectively remove both stationary and highly nonstationary noise while preserving the important features of the true forced response signals. The simulation results show that the proposed noise removal technique improves the accuracy of the estimated modal parameters using only one impulse response signal. The experimental results show that the proposed method can accurately identify a natural frequency that is very close to a strong interference frequency in the modal test of a 600-MW generator casing. Journal Article Structural Control and Health Monitoring e2019 15452255 hybrid method; low SNR; MMSE-STSA estimator; modal identification; WIENER-STSA estimator 31 12 2017 2017-12-31 10.1002/stc.2019 COLLEGE NANME Science and Engineering - Faculty COLLEGE CODE FGSEN Swansea University 2017-07-31T14:43:15.3275807 2017-05-17T15:33:05.8197703 Faculty of Science and Engineering School of Engineering and Applied Sciences - Uncategorised Dan He 1 Xiufeng Wang 2 Michael Friswell 3 Jing Lin 4 0033695-17052017160337.pdf he2017.pdf 2017-05-17T16:03:37.7930000 Output 2304842 application/pdf Accepted Manuscript true 2018-05-05T00:00:00.0000000 true eng
title Identification of modal parameters from noisy transient response signals
spellingShingle Identification of modal parameters from noisy transient response signals
Michael Friswell
title_short Identification of modal parameters from noisy transient response signals
title_full Identification of modal parameters from noisy transient response signals
title_fullStr Identification of modal parameters from noisy transient response signals
title_full_unstemmed Identification of modal parameters from noisy transient response signals
title_sort Identification of modal parameters from noisy transient response signals
author_id_str_mv 5894777b8f9c6e64bde3568d68078d40
author_id_fullname_str_mv 5894777b8f9c6e64bde3568d68078d40_***_Michael Friswell
author Michael Friswell
author2 Dan He
Xiufeng Wang
Michael Friswell
Jing Lin
format Journal article
container_title Structural Control and Health Monitoring
container_start_page e2019
publishDate 2017
institution Swansea University
issn 15452255
doi_str_mv 10.1002/stc.2019
college_str Faculty of Science and Engineering
hierarchytype
hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Engineering and Applied Sciences - Uncategorised{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Engineering and Applied Sciences - Uncategorised
document_store_str 1
active_str 0
description In the process of impact testing of large-scale mechanical equipment, the measured forced response signals are often polluted by strong background noise. The forced response signal has a low signal-to-noise ratio, and this makes it difficult to accurately estimate the modal parameters. To solve this problem, the mean averaging of repeatedly measured frequency response function estimates is often employed in practical applications. However, a large number of impact tests are not practical for the modal testing of large-scale mechanical equipment. The primary objective of this paper is to reduce the averaging operation and improve the accuracy of the modal identification by using a noise removal technique. A hybrid denoising method is proposed by combining the Wiener and improved minimum mean-square-error short-time spectral amplitude estimators. The proposed method can effectively remove both stationary and highly nonstationary noise while preserving the important features of the true forced response signals. The simulation results show that the proposed noise removal technique improves the accuracy of the estimated modal parameters using only one impulse response signal. The experimental results show that the proposed method can accurately identify a natural frequency that is very close to a strong interference frequency in the modal test of a 600-MW generator casing.
published_date 2017-12-31T03:41:43Z
_version_ 1763751917127729152
score 11.035634

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