Matches in SemOpenAlex for { <https://semopenalex.org/work/W3138974471> ?p ?o ?g. }
Showing items 1 to 89 of
89
with 100 items per page.
- W3138974471 abstract "In order to extract the fault features of mechanical equipment submerged by strong background noise, a general subspace denoising algorithm based on Singular value decomposition (SVD) is used to process the signal, that is, μ-SVD denoising algorithm. This algorithm overcomes the disadvantage of traditional wavelet threshold denoising algorithm, which only deals with the wavelet coefficients point by point and ignores the whole structure of the wavelet coefficients. The traditional SVD denoising algorithm is a special case when Lagrange multiplier μ = 0 in μ-SVD denoising algorithm. μ-SVD denoising algorithm includes filter factor, which can suppress the contribution of singular value dominated by noise contribution to the signal after denoising. The parameter selection method of μ-SVD denoising algorithm is discussed, and the influence of denoising order and Lagrange multiplier on denoising effect is emphatically studied. The test results of gear fault simulation signal and gear fault vibration signal at the early stage show that μ-SVD denoising algorithm is better than traditional SVD denoising algorithm in denoising effect. It can extract gear fault features better under strong background noise." @default.
- W3138974471 created "2021-03-29" @default.
- W3138974471 creator A5001683138 @default.
- W3138974471 creator A5003854826 @default.
- W3138974471 creator A5029926368 @default.
- W3138974471 creator A5033041049 @default.
- W3138974471 creator A5058411857 @default.
- W3138974471 creator A5078487642 @default.
- W3138974471 date "2019-10-09" @default.
- W3138974471 modified "2023-09-26" @default.
- W3138974471 title "APPLICATION OF QUANTITATIVE ANALYSIS OF GEAR FAULT IN μ-SVD NOISE REDUCTION ALGORITHM DIAGNOSIS" @default.
- W3138974471 cites W2002943794 @default.
- W3138974471 cites W2141023313 @default.
- W3138974471 cites W2150639927 @default.
- W3138974471 cites W2262538465 @default.
- W3138974471 cites W2509906701 @default.
- W3138974471 cites W2788705187 @default.
- W3138974471 cites W2797347777 @default.
- W3138974471 cites W2894227938 @default.
- W3138974471 doi "https://doi.org/10.52292/j.laar.2018.233" @default.
- W3138974471 hasPublicationYear "2019" @default.
- W3138974471 type Work @default.
- W3138974471 sameAs 3138974471 @default.
- W3138974471 citedByCount "0" @default.
- W3138974471 crossrefType "journal-article" @default.
- W3138974471 hasAuthorship W3138974471A5001683138 @default.
- W3138974471 hasAuthorship W3138974471A5003854826 @default.
- W3138974471 hasAuthorship W3138974471A5029926368 @default.
- W3138974471 hasAuthorship W3138974471A5033041049 @default.
- W3138974471 hasAuthorship W3138974471A5058411857 @default.
- W3138974471 hasAuthorship W3138974471A5078487642 @default.
- W3138974471 hasBestOaLocation W31389744711 @default.
- W3138974471 hasConcept C106131492 @default.
- W3138974471 hasConcept C11413529 @default.
- W3138974471 hasConcept C115961682 @default.
- W3138974471 hasConcept C126255220 @default.
- W3138974471 hasConcept C153180895 @default.
- W3138974471 hasConcept C154945302 @default.
- W3138974471 hasConcept C163294075 @default.
- W3138974471 hasConcept C202474056 @default.
- W3138974471 hasConcept C22789450 @default.
- W3138974471 hasConcept C23431618 @default.
- W3138974471 hasConcept C2777121530 @default.
- W3138974471 hasConcept C2781238097 @default.
- W3138974471 hasConcept C293773 @default.
- W3138974471 hasConcept C30814859 @default.
- W3138974471 hasConcept C31972630 @default.
- W3138974471 hasConcept C33923547 @default.
- W3138974471 hasConcept C41008148 @default.
- W3138974471 hasConcept C47432892 @default.
- W3138974471 hasConcept C73684929 @default.
- W3138974471 hasConcept C99498987 @default.
- W3138974471 hasConceptScore W3138974471C106131492 @default.
- W3138974471 hasConceptScore W3138974471C11413529 @default.
- W3138974471 hasConceptScore W3138974471C115961682 @default.
- W3138974471 hasConceptScore W3138974471C126255220 @default.
- W3138974471 hasConceptScore W3138974471C153180895 @default.
- W3138974471 hasConceptScore W3138974471C154945302 @default.
- W3138974471 hasConceptScore W3138974471C163294075 @default.
- W3138974471 hasConceptScore W3138974471C202474056 @default.
- W3138974471 hasConceptScore W3138974471C22789450 @default.
- W3138974471 hasConceptScore W3138974471C23431618 @default.
- W3138974471 hasConceptScore W3138974471C2777121530 @default.
- W3138974471 hasConceptScore W3138974471C2781238097 @default.
- W3138974471 hasConceptScore W3138974471C293773 @default.
- W3138974471 hasConceptScore W3138974471C30814859 @default.
- W3138974471 hasConceptScore W3138974471C31972630 @default.
- W3138974471 hasConceptScore W3138974471C33923547 @default.
- W3138974471 hasConceptScore W3138974471C41008148 @default.
- W3138974471 hasConceptScore W3138974471C47432892 @default.
- W3138974471 hasConceptScore W3138974471C73684929 @default.
- W3138974471 hasConceptScore W3138974471C99498987 @default.
- W3138974471 hasLocation W31389744711 @default.
- W3138974471 hasOpenAccess W3138974471 @default.
- W3138974471 hasPrimaryLocation W31389744711 @default.
- W3138974471 hasRelatedWork W11499724 @default.
- W3138974471 hasRelatedWork W12235189 @default.
- W3138974471 hasRelatedWork W12245276 @default.
- W3138974471 hasRelatedWork W12549230 @default.
- W3138974471 hasRelatedWork W1941230 @default.
- W3138974471 hasRelatedWork W2753653 @default.
- W3138974471 hasRelatedWork W3953451 @default.
- W3138974471 hasRelatedWork W4291971 @default.
- W3138974471 hasRelatedWork W4830638 @default.
- W3138974471 hasRelatedWork W7564530 @default.
- W3138974471 isParatext "false" @default.
- W3138974471 isRetracted "false" @default.
- W3138974471 magId "3138974471" @default.
- W3138974471 workType "article" @default.