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Block-MUSIC in blade tip timing: Performance study of block snapshot matrix
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Xi An Jiao Tong Univ, Sch Mech Engn, Xian 710049, Peoples R China..ORCID iD: 0000-0002-5455-942X
Xi An Jiao Tong Univ, Sch Mech Engn, Xian 710049, Peoples R China..
Xi An Jiao Tong Univ, Sch Mech Engn, Xian 710049, Peoples R China..
Xi An Jiao Tong Univ, Sch Mech Engn, Xian 710049, Peoples R China..
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2023 (English)In: Mechanical systems and signal processing, ISSN 0888-3270, E-ISSN 1096-1216, Vol. 198, article id 110374Article in journal (Refereed) Published
Abstract [en]

Periodic nonuniform sampling can be viewed as a combination of uniform sampling and non-uniform sampling. Blade tip timing (BTT) technique conforms to this sampling pattern, where non-uniformity and uniformity are caused by non-uniform probe layout and rotational motion, respectively. By introducing block structure into multiple signal classification (MUSIC) for the extension of snapshot matrix, Block-MUSIC has been proved to be anti-aliasing and can filter out synchronous frequency in BTT application. However, the theoretical justification of the block structure is lacking. In this paper, a performance study of Block-MUSIC is conducted from three aspects: identifiability, stability, and resolution. Additionally, a pseudo 2-dimensional signal model is proposed to represent the periodic nonuniform sampled signal under the assumption of equally spaced probes. To support the performance study, simulations and experiments are carried out to demonstrate how the size of the snapshot matrix, the noise level, and the amplitude of sinusoidal signals affect algorithm performance. Simulation codes are available at https://github.com/superjdg/block-music4btt.

Place, publisher, year, edition, pages
Elsevier, 2023. Vol. 198, article id 110374
Keywords [en]
Periodic nonuniform sampling, MUSIC, Blade tip timing, Spectrum analysis, Vandermonde decomposition
National Category
Signal Processing
Identifiers
URN: urn:nbn:se:uu:diva-506569DOI: 10.1016/j.ymssp.2023.110374ISI: 001000588600001OAI: oai:DiVA.org:uu-506569DiVA, id: diva2:1777167
Available from: 2023-06-29 Created: 2023-06-29 Last updated: 2023-06-29Bibliographically approved

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Wang, Zengkun

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