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Stable subspace dimension reduced MUSIC for blade tip timing
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- Yang, Zhi-Bo (author)
- Xi An Jiao Tong Univ, Sch Mech Engn, Xian 710049, Peoples R China.
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- Wang, Zeng-Kun (author)
- Uppsala universitet,Avdelningen för systemteknik,Xi An Jiao Tong Univ, Sch Mech Engn, Xian 710049, Peoples R China.
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- Li, Hao-Qi (author)
- Xi An Jiao Tong Univ, Sch Mech Engn, Xian 710049, Peoples R China.
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- Xi An Jiao Tong Univ, Sch Mech Engn, Xian 710049, Peoples R China Avdelningen för systemteknik (creator_code:org_t)
- Elsevier, 2023
- 2023
- English.
- In: Journal of Sound and Vibration. - : Elsevier. - 0022-460X .- 1095-8568. ; 545
- Journal article (peer-reviewed)
Abstract
Subject headings
Close
- Multiple signal classification (MUSIC) is a parameter extraction method for blade tip timing to suppress the under-sampled problem. Its faster version, subspace dimension reduced MUSIC (SDR-MUSIC), has been proposed, which uses a single noise vector instead of the whole noise subspace to reduce computational complexity. However, the principle of random noise vector selection leads to instability in frequency identification. Therefore, we replace the random vector with the min-norm vector to obtain a stable SDR-MUSIC (SSDR-MUSIC). The superiority of the min-norm vector can be mathematically proven by the location of the zeros of the polynomial. Simulated and experimental tests indicate that SSDR-MUSIC gains stability when the min-norm vector is used while maintaining computational efficiency.
Subject headings
- HUMANIORA -- Konst -- Musikvetenskap (hsv//swe)
- HUMANITIES -- Arts -- Musicology (hsv//eng)
Keyword
- MUSIC
- Blade tip timing
- Condition monitoring
- Spectrum analysis
- Min-norm vector
Publication and Content Type
- ref (subject category)
- art (subject category)
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- Yang, Zhi-Bo
- Wang, Zeng-Kun
- Li, Hao-Qi
- Tian, Shao-Hua
- Chen, Xue-Feng
- About the subject
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- HUMANITIES
- HUMANITIES
- and Arts
- and Musicology
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- Uppsala University
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