Understanding and managing identification uncertainty of close modes in operational modal analysis
dc.contributor.author | Au, S-K | |
dc.contributor.author | Brownjohn, JMW | |
dc.contributor.author | Li, B | |
dc.contributor.author | Raby, A | |
dc.date.accessioned | 2020-06-15T11:10:41Z | |
dc.date.issued | 2020-08-05 | |
dc.description.abstract | Close modes are much more difficult to identify than well-separated modes and their identification (ID) results often have significantly larger uncertainty or variability. The situation becomes even more challenging in operational modal analysis (OMA), which is currently the most economically viable means for obtaining in-situ dynamic properties of large civil structures and where ID uncertainty management is most needed. To understand ID uncertainty and manage it in field test planning, this work develops the ‘uncertainty law’ for close modes, i.e., closed form analytical expressions for the remaining uncertainty of modal parameters identified using output-only ambient vibration data. The expressions reveal a fundamental definition that quantifies ‘how close is close’ and demystify the roles of various governing factors. The results are verified with synthetic, laboratory and field data. Statistics of governing factors from field data reveal OMA challenges in different situations, now accountable within a coherent probabilistic framework. Recommendations are made for planning ambient vibration tests taking close modes into account. Up to modelling assumptions and the use of probability, the uncertainty law dictates the achievable precision of modal properties regardless of the ID algorithm used. The mathematical theory behind the results in this paper is presented in a companion paper. | en_GB |
dc.description.sponsorship | Engineering and Physical Sciences Research Council (EPSRC) | en_GB |
dc.identifier.citation | Vol. 147, article 107018 | en_GB |
dc.identifier.doi | 10.1016/j.ymssp.2020.107018 | |
dc.identifier.grantnumber | EP/N017897/1 | en_GB |
dc.identifier.grantnumber | EP/N017803 | en_GB |
dc.identifier.grantnumber | EP/N022947/1 | en_GB |
dc.identifier.grantnumber | EP/N022955/1 | en_GB |
dc.identifier.uri | http://hdl.handle.net/10871/121440 | |
dc.language.iso | en | en_GB |
dc.publisher | Elsevier | en_GB |
dc.rights | © 2020 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/) | |
dc.subject | ambient modal identification | en_GB |
dc.subject | BAYOMA | en_GB |
dc.subject | close modes | en_GB |
dc.subject | Fisher Information Matrix | en_GB |
dc.subject | operational modal analysis | en_GB |
dc.subject | uncertainty law | en_GB |
dc.title | Understanding and managing identification uncertainty of close modes in operational modal analysis | en_GB |
dc.type | Article | en_GB |
dc.date.available | 2020-06-15T11:10:41Z | |
dc.identifier.issn | 0888-3270 | |
dc.description | This is the final version. Available on open access from Elsevier via the DOI in this record. | en_GB |
dc.identifier.journal | Mechanical Systems and Signal Processing | en_GB |
dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | en_GB |
dcterms.dateAccepted | 2020-05-29 | |
exeter.funder | ::Engineering and Physical Sciences Research Council (EPSRC) | en_GB |
exeter.funder | ::Engineering and Physical Sciences Research Council (EPSRC) | en_GB |
rioxxterms.version | VoR | en_GB |
rioxxterms.licenseref.startdate | 2020-05-29 | |
rioxxterms.type | Journal Article/Review | en_GB |
refterms.dateFCD | 2020-06-15T10:24:00Z | |
refterms.versionFCD | AM | |
refterms.dateFOA | 2020-08-06T14:27:01Z | |
refterms.panel | B | en_GB |
Files in this item
This item appears in the following Collection(s)
Except where otherwise noted, this item's licence is described as © 2020 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/)