A tacholess order tracking methodology based on a probabilistic approach to incorporate angular acceleration information into the maxima tracking process

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dc.contributor.author Schmidt, Stephan
dc.contributor.author Heyns, P.S. (Philippus Stephanus)
dc.contributor.author De Villiers, Johan Pieter
dc.date.accessioned 2017-09-21T09:36:04Z
dc.date.issued 2018-02
dc.description.abstract Rotational speed or phase measurements are important in the rotating machine condition monitoring field. Rotating machines often operate in the presence of fluctuating rotational speeds and its components generate signals which are periodical in the angle domain. Hence, it is essential to perform order tracking when condition monitoring is performed under varying speed conditions. Computed order tracking is performed with a measured tachometer signal and it requires additional hardware to be installed on the machine, which may not be feasible nor practical. This article presents a tacholess order tracking method which is capable of accurately estimating the phase of a shaft of interest in the presence of large angular accelerations and noise. An improved maxima tracking procedure is used with an angular-displacement Vold-Kalman filter and the Hilbert transform to estimate the instantaneous phase of the shaft under consideration. The estimated instantaneous phase is used to resample the vibration signal from the time to the angle domain. The proposed tacholess order tracking technique is critically investigated on three numerical and three experimental rotational speed profiles. The minimum and maximum phase error obtained for the experimental data was 0.037697 and 0.05022 radians respectively, which highlights the potential of the technique. en_ZA
dc.description.department Electrical, Electronic and Computer Engineering en_ZA
dc.description.department Mechanical and Aeronautical Engineering en_ZA
dc.description.embargo 2019-02-01
dc.description.librarian hj2017 en_ZA
dc.description.sponsorship Eskom Power Plant Engineering Institute (EPPEI) en_ZA
dc.description.uri http://www.elsevier.com/locate/jnlabr/ymssp en_ZA
dc.identifier.citation Schmidt, S., Heyns, P.S. & De Villiers, J.P. 2018, 'A tacholess order tracking methodology based on a probabilistic approach to incorporate angular acceleration information into the maxima tracking process', Mechanical Systems and Signal Processing, vol. 100, pp. 630-646. en_ZA
dc.identifier.issn 0888-3270 (print)
dc.identifier.issn 1096-1216 (online)
dc.identifier.other 10.1016/j.ymssp.2017.07.053
dc.identifier.uri http://hdl.handle.net/2263/62492
dc.language.iso en en_ZA
dc.publisher Elsevier en_ZA
dc.rights © 2017 Elsevier Ltd. All rights reserved. Notice : this is the author’s version of a work that was accepted for publication in Mechanical Systems and Signal Processing. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. A definitive version was subsequently published in Mechanical Systems and Signal Processing, vol. 100, pp. 630-646. 2018. doi : 10.1016/j.ymssp.2017.07.053. en_ZA
dc.subject Tacholess order tracking en_ZA
dc.subject Maxima tracking en_ZA
dc.subject Vold-Kalman filter en_ZA
dc.subject Rotational speed estimation en_ZA
dc.subject Linear regression en_ZA
dc.subject Varying speed conditions en_ZA
dc.subject Rotational speed en_ZA
dc.subject Probabilistic approach en_ZA
dc.subject Order tracking technique en_ZA
dc.subject Computed order tracking en_ZA
dc.subject Speed en_ZA
dc.subject Shaft displacement en_ZA
dc.subject Rotating machinery en_ZA
dc.subject Mathematical transformations en_ZA
dc.subject Condition monitoring en_ZA
dc.subject.other Engineering, built environment and information technology articles SDG-09
dc.subject.other SDG-09: Industry, innovation and infrastructure
dc.subject.other Engineering, built environment and information technology articles SDG-12
dc.subject.other SDG-12: Responsible consumption and production
dc.title A tacholess order tracking methodology based on a probabilistic approach to incorporate angular acceleration information into the maxima tracking process en_ZA
dc.type Postprint Article en_ZA


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