Robust Spectral Peaks Detection in Vibration and Acoustic Signals
This article brings a practical solution to the problem of spectral peak detection in nonuniform spectra. It applies a robust probabilistic approach that fits the histogram of trimmed spectral data with a truncated Gamma distribution. The estimated distribution parameters are used to derive a thresh...
Gespeichert in:
Veröffentlicht in: | IEEE transactions on instrumentation and measurement 2022, Vol.71, p.1-13 |
---|---|
Hauptverfasser: | , , , , , |
Format: | Artikel |
Sprache: | eng |
Schlagworte: | |
Online-Zugang: | Volltext bestellen |
Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
container_end_page | 13 |
---|---|
container_issue | |
container_start_page | 1 |
container_title | IEEE transactions on instrumentation and measurement |
container_volume | 71 |
creator | Hawwari, Yasmine Antoni, Jerome Andre, Hugo Marnissi, Yosra Abboud, Dany El-Badaoui, Mohammed |
description | This article brings a practical solution to the problem of spectral peak detection in nonuniform spectra. It applies a robust probabilistic approach that fits the histogram of trimmed spectral data with a truncated Gamma distribution. The estimated distribution parameters are used to derive a threshold through a hypothesis test in the presence of peaks. The proposed approach gains its robustness from the formulation of the no-peak distribution, while no knowledge is available about the amount of peaks in spectral data. The authors propose a preprocessing step to cope with a nonuniform spectrum. The proposed methodology is validated on both simulated and experimental vibration and acoustic signals. |
doi_str_mv | 10.1109/TIM.2022.3187742 |
format | Article |
fullrecord | <record><control><sourceid>proquest_RIE</sourceid><recordid>TN_cdi_crossref_primary_10_1109_TIM_2022_3187742</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><ieee_id>9812739</ieee_id><sourcerecordid>2689807504</sourcerecordid><originalsourceid>FETCH-LOGICAL-c325t-463bc7089f775b0492111411710485b3cc15a98f90f15dd1230a9017c03903e23</originalsourceid><addsrcrecordid>eNo9kM1Lw0AUxBdRsFbvgpeAJw-p7-1HdvcY6kcLFcVWr8tmu9GtNambVPC_N7HF0zCP3wyPIeQcYYQI-noxfRhRoHTEUEnJ6QEZoBAy1VlGD8kAAFWquciOyUnTrABAZlwOSP5cF9umTeYb79po18mTtx9NcuPbzoe6SkKVvIYi2j9jq2WSu7oLBJfMw1tl180pOSo78Wd7HZKXu9vFeJLOHu-n43yWOkZFm_KMFU6C0qWUogCuKSJyRInAlSiYcyisVqWGEsVyiZSB1YDSAdPAPGVDcrXrfbdrs4nh08YfU9tgJvnM9DdgkmeQ4Td27OWO3cT6a-ub1qzqbey_NTRTWoEUwDsKdpSLddNEX_7XIph-VNONavpRzX7ULnKxiwTv_T-uFVLJNPsFDa1u7g</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2689807504</pqid></control><display><type>article</type><title>Robust Spectral Peaks Detection in Vibration and Acoustic Signals</title><source>IEEE Electronic Library (IEL)</source><creator>Hawwari, Yasmine ; Antoni, Jerome ; Andre, Hugo ; Marnissi, Yosra ; Abboud, Dany ; El-Badaoui, Mohammed</creator><creatorcontrib>Hawwari, Yasmine ; Antoni, Jerome ; Andre, Hugo ; Marnissi, Yosra ; Abboud, Dany ; El-Badaoui, Mohammed</creatorcontrib><description>This article brings a practical solution to the problem of spectral peak detection in nonuniform spectra. It applies a robust probabilistic approach that fits the histogram of trimmed spectral data with a truncated Gamma distribution. The estimated distribution parameters are used to derive a threshold through a hypothesis test in the presence of peaks. The proposed approach gains its robustness from the formulation of the no-peak distribution, while no knowledge is available about the amount of peaks in spectral data. The authors propose a preprocessing step to cope with a nonuniform spectrum. The proposed methodology is validated on both simulated and experimental vibration and acoustic signals.</description><identifier>ISSN: 0018-9456</identifier><identifier>EISSN: 1557-9662</identifier><identifier>DOI: 10.1109/TIM.2022.3187742</identifier><identifier>CODEN: IEIMAO</identifier><language>eng</language><publisher>New York: IEEE</publisher><subject>Background noise ; Colored noise ; Engineering Sciences ; Estimation ; Histograms ; Nonwhite noise ; Probability distribution functions ; Robustness ; spectral peaks ; Standardization ; Statistical analysis ; trimmed data ; truncated gamma distribution ; Vibration ; vibration/acoustic signals ; Vibrations ; White noise</subject><ispartof>IEEE transactions on instrumentation and measurement, 2022, Vol.71, p.1-13</ispartof><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2022</rights><rights>Distributed under a Creative Commons Attribution 4.0 International License</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c325t-463bc7089f775b0492111411710485b3cc15a98f90f15dd1230a9017c03903e23</citedby><cites>FETCH-LOGICAL-c325t-463bc7089f775b0492111411710485b3cc15a98f90f15dd1230a9017c03903e23</cites><orcidid>0000-0003-4659-7132 ; 0000-0003-1150-563X ; 0000-0002-5166-4443 ; 0000-0001-5323-3746 ; 0000-0002-1251-2180 ; 0000-0003-4128-476X</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/9812739$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>230,314,776,780,792,881,4010,27900,27901,27902,54733</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/9812739$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc><backlink>$$Uhttps://hal.science/hal-03746061$$DView record in HAL$$Hfree_for_read</backlink></links><search><creatorcontrib>Hawwari, Yasmine</creatorcontrib><creatorcontrib>Antoni, Jerome</creatorcontrib><creatorcontrib>Andre, Hugo</creatorcontrib><creatorcontrib>Marnissi, Yosra</creatorcontrib><creatorcontrib>Abboud, Dany</creatorcontrib><creatorcontrib>El-Badaoui, Mohammed</creatorcontrib><title>Robust Spectral Peaks Detection in Vibration and Acoustic Signals</title><title>IEEE transactions on instrumentation and measurement</title><addtitle>TIM</addtitle><description>This article brings a practical solution to the problem of spectral peak detection in nonuniform spectra. It applies a robust probabilistic approach that fits the histogram of trimmed spectral data with a truncated Gamma distribution. The estimated distribution parameters are used to derive a threshold through a hypothesis test in the presence of peaks. The proposed approach gains its robustness from the formulation of the no-peak distribution, while no knowledge is available about the amount of peaks in spectral data. The authors propose a preprocessing step to cope with a nonuniform spectrum. The proposed methodology is validated on both simulated and experimental vibration and acoustic signals.</description><subject>Background noise</subject><subject>Colored noise</subject><subject>Engineering Sciences</subject><subject>Estimation</subject><subject>Histograms</subject><subject>Nonwhite noise</subject><subject>Probability distribution functions</subject><subject>Robustness</subject><subject>spectral peaks</subject><subject>Standardization</subject><subject>Statistical analysis</subject><subject>trimmed data</subject><subject>truncated gamma distribution</subject><subject>Vibration</subject><subject>vibration/acoustic signals</subject><subject>Vibrations</subject><subject>White noise</subject><issn>0018-9456</issn><issn>1557-9662</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNo9kM1Lw0AUxBdRsFbvgpeAJw-p7-1HdvcY6kcLFcVWr8tmu9GtNambVPC_N7HF0zCP3wyPIeQcYYQI-noxfRhRoHTEUEnJ6QEZoBAy1VlGD8kAAFWquciOyUnTrABAZlwOSP5cF9umTeYb79po18mTtx9NcuPbzoe6SkKVvIYi2j9jq2WSu7oLBJfMw1tl180pOSo78Wd7HZKXu9vFeJLOHu-n43yWOkZFm_KMFU6C0qWUogCuKSJyRInAlSiYcyisVqWGEsVyiZSB1YDSAdPAPGVDcrXrfbdrs4nh08YfU9tgJvnM9DdgkmeQ4Td27OWO3cT6a-ub1qzqbey_NTRTWoEUwDsKdpSLddNEX_7XIph-VNONavpRzX7ULnKxiwTv_T-uFVLJNPsFDa1u7g</recordid><startdate>2022</startdate><enddate>2022</enddate><creator>Hawwari, Yasmine</creator><creator>Antoni, Jerome</creator><creator>Andre, Hugo</creator><creator>Marnissi, Yosra</creator><creator>Abboud, Dany</creator><creator>El-Badaoui, Mohammed</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. (IEEE)</general><general>Institute of Electrical and Electronics Engineers</general><scope>97E</scope><scope>RIA</scope><scope>RIE</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SP</scope><scope>7U5</scope><scope>8FD</scope><scope>L7M</scope><scope>1XC</scope><orcidid>https://orcid.org/0000-0003-4659-7132</orcidid><orcidid>https://orcid.org/0000-0003-1150-563X</orcidid><orcidid>https://orcid.org/0000-0002-5166-4443</orcidid><orcidid>https://orcid.org/0000-0001-5323-3746</orcidid><orcidid>https://orcid.org/0000-0002-1251-2180</orcidid><orcidid>https://orcid.org/0000-0003-4128-476X</orcidid></search><sort><creationdate>2022</creationdate><title>Robust Spectral Peaks Detection in Vibration and Acoustic Signals</title><author>Hawwari, Yasmine ; Antoni, Jerome ; Andre, Hugo ; Marnissi, Yosra ; Abboud, Dany ; El-Badaoui, Mohammed</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c325t-463bc7089f775b0492111411710485b3cc15a98f90f15dd1230a9017c03903e23</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Background noise</topic><topic>Colored noise</topic><topic>Engineering Sciences</topic><topic>Estimation</topic><topic>Histograms</topic><topic>Nonwhite noise</topic><topic>Probability distribution functions</topic><topic>Robustness</topic><topic>spectral peaks</topic><topic>Standardization</topic><topic>Statistical analysis</topic><topic>trimmed data</topic><topic>truncated gamma distribution</topic><topic>Vibration</topic><topic>vibration/acoustic signals</topic><topic>Vibrations</topic><topic>White noise</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Hawwari, Yasmine</creatorcontrib><creatorcontrib>Antoni, Jerome</creatorcontrib><creatorcontrib>Andre, Hugo</creatorcontrib><creatorcontrib>Marnissi, Yosra</creatorcontrib><creatorcontrib>Abboud, Dany</creatorcontrib><creatorcontrib>El-Badaoui, Mohammed</creatorcontrib><collection>IEEE All-Society Periodicals Package (ASPP) 2005-present</collection><collection>IEEE All-Society Periodicals Package (ASPP) 1998-Present</collection><collection>IEEE Electronic Library (IEL)</collection><collection>CrossRef</collection><collection>Electronics & Communications Abstracts</collection><collection>Solid State and Superconductivity Abstracts</collection><collection>Technology Research Database</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Hyper Article en Ligne (HAL)</collection><jtitle>IEEE transactions on instrumentation and measurement</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Hawwari, Yasmine</au><au>Antoni, Jerome</au><au>Andre, Hugo</au><au>Marnissi, Yosra</au><au>Abboud, Dany</au><au>El-Badaoui, Mohammed</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Robust Spectral Peaks Detection in Vibration and Acoustic Signals</atitle><jtitle>IEEE transactions on instrumentation and measurement</jtitle><stitle>TIM</stitle><date>2022</date><risdate>2022</risdate><volume>71</volume><spage>1</spage><epage>13</epage><pages>1-13</pages><issn>0018-9456</issn><eissn>1557-9662</eissn><coden>IEIMAO</coden><abstract>This article brings a practical solution to the problem of spectral peak detection in nonuniform spectra. It applies a robust probabilistic approach that fits the histogram of trimmed spectral data with a truncated Gamma distribution. The estimated distribution parameters are used to derive a threshold through a hypothesis test in the presence of peaks. The proposed approach gains its robustness from the formulation of the no-peak distribution, while no knowledge is available about the amount of peaks in spectral data. The authors propose a preprocessing step to cope with a nonuniform spectrum. The proposed methodology is validated on both simulated and experimental vibration and acoustic signals.</abstract><cop>New York</cop><pub>IEEE</pub><doi>10.1109/TIM.2022.3187742</doi><tpages>13</tpages><orcidid>https://orcid.org/0000-0003-4659-7132</orcidid><orcidid>https://orcid.org/0000-0003-1150-563X</orcidid><orcidid>https://orcid.org/0000-0002-5166-4443</orcidid><orcidid>https://orcid.org/0000-0001-5323-3746</orcidid><orcidid>https://orcid.org/0000-0002-1251-2180</orcidid><orcidid>https://orcid.org/0000-0003-4128-476X</orcidid></addata></record> |
fulltext | fulltext_linktorsrc |
identifier | ISSN: 0018-9456 |
ispartof | IEEE transactions on instrumentation and measurement, 2022, Vol.71, p.1-13 |
issn | 0018-9456 1557-9662 |
language | eng |
recordid | cdi_crossref_primary_10_1109_TIM_2022_3187742 |
source | IEEE Electronic Library (IEL) |
subjects | Background noise Colored noise Engineering Sciences Estimation Histograms Nonwhite noise Probability distribution functions Robustness spectral peaks Standardization Statistical analysis trimmed data truncated gamma distribution Vibration vibration/acoustic signals Vibrations White noise |
title | Robust Spectral Peaks Detection in Vibration and Acoustic Signals |
url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-02-02T20%3A27%3A37IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_RIE&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Robust%20Spectral%20Peaks%20Detection%20in%20Vibration%20and%20Acoustic%20Signals&rft.jtitle=IEEE%20transactions%20on%20instrumentation%20and%20measurement&rft.au=Hawwari,%20Yasmine&rft.date=2022&rft.volume=71&rft.spage=1&rft.epage=13&rft.pages=1-13&rft.issn=0018-9456&rft.eissn=1557-9662&rft.coden=IEIMAO&rft_id=info:doi/10.1109/TIM.2022.3187742&rft_dat=%3Cproquest_RIE%3E2689807504%3C/proquest_RIE%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2689807504&rft_id=info:pmid/&rft_ieee_id=9812739&rfr_iscdi=true |