Development of a criteria for squeal noise detection applicable to on-board noise monitoring

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dc.contributor.authorLennartsson, Jesper
dc.contributor.authorVedin, Axel
dc.contributor.departmentChalmers tekniska högskola / Institutionen för arkitektur och samhällsbyggnadsteknik (ACE)sv
dc.contributor.departmentChalmers tekniska högskola / Institutionen för arkitektur och samhällsbyggnadsteknik (ACE)en
dc.contributor.examinerPieringer, Astrid
dc.date.accessioned2024-07-01T08:36:58Z
dc.date.available2024-07-01T08:36:58Z
dc.date.issued2024
dc.date.submitted
dc.description.abstractRailway vehicles may generate a high tonal noise commonly referred to as curve squeal during navigation through curves. Typically, this noise is characterized by a single high pitch frequency accompanied by a few overtones. Its tonal character makes it more irritating for a listener than a broad-band noise at the same sound pressure level. This is a contributing factor to why curve squeal is perceived as being so disturbing. Curve squeal is produced as a result of self-excited wheel vibrations which are trig gered by large magnitude lateral forces developed in the contact between the inner wheel and the low rail. Another type of squeal noise is called flange squeal which instead originates from the outer wheel. In comparison to the tonal curve squeal, flange squeal has a more broadband character. This study is based on noise data recorded by an onboard monitoring system during one month of traffic on the Green line of the Stockholm metro. Five different meth ods of detecting curve and flange squeal are evaluated. The methods accounted for are: (A) an implementation of the algorithm for curve squeal detection in operation at the Stockholm metro today, (B) an algorithm taken from literature developed by a research group at the university of Wollongong, Australia, (C) the application of a tonality module available in the commercial software ArtemiS SUITE, (D) a method devised by the authors. Finally, (E) a Hybrid method that combines method A and B. Method A is based on the sound pressure level difference between outer and inner wheels while B evaluates the spectrum of the inner wheel and looks for the highest 1/24 octave band to compare against a set criterion. C calculates the tonality of the inner wheel and marks when it is above a set limit. D calculates the sound pressure level and compares it against a threshold. All methods except C are implemented in Matlab whereas the software Artemis SUITE is used to evaluate method C. The results showed that most of the methods did not produce accurate results rather containing many false positives. Method A performed best with respect to curve squeal detection whereas the so called Hybrid algorithm, containing criteria from both methods A and B, showed best abilities to detect flange squeal.
dc.identifier.coursecodeACEX30
dc.identifier.urihttp://hdl.handle.net/20.500.12380/308154
dc.language.isoeng
dc.setspec.uppsokTechnology
dc.subjectSqueal noise
dc.subjectcurve squeal
dc.subjectflange squeal
dc.subjectdetection algorithms
dc.titleDevelopment of a criteria for squeal noise detection applicable to on-board noise monitoring
dc.type.degreeExamensarbete för masterexamensv
dc.type.degreeMaster's Thesisen
dc.type.uppsokH
local.programmeSound and vibration (MPSOV), MSc
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