FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4380091963> ?p ?o ?g. }

• W4380091963 endingPage "110492" @default.
• W4380091963 startingPage "110492" @default.
• W4380091963 abstract "Interpretable learning models become an emerging topic in the domain of machine condition monitoring to connect signal processing algorithms with statistical learning and machine learning. Compared with traditional signal processing algorithms that show strong ability in data analysis, signal-processing-related interpretable learning models could generate interpretable learnable weights/parameters as advanced physically interpretable fault features for both machine condition monitoring and fault diagnosis. It is well-known that linear discriminant analysis (LDA) is one of the most popular and interpretable algorithms for machine condition monitoring. However, this popular algorithm needs Gaussian assumptions in their derivations and parameter estimations. In this paper, Gaussian assumptions-free interpretable LDA is proposed as an interpretable learning model to physically locate informative frequency bands and fault characteristic frequencies for machine condition monitoring. Firstly, statistical decision theory is introduced to connect the nature of regression with that of classification, which poses a foundation for the Gaussian assumptions-free interpretable LDA for machine condition monitoring. Secondly, two propositions are given to mathematically show that LDA can be realized by an equivalent linear regression analysis, which provides a perspective for the Gaussian assumptions-free interpretable LDA for simultaneous machine condition monitoring and fault diagnosis. Finally, linear regression analysis with a sparse Lp-norm regularization term is introduced to realize the Gaussian assumptions-free interpretable LDA for physically locating informative frequency bands and fault characteristic frequencies for machine condition monitoring. Two case studies are provided as illustrative examples to experimentally demonstrate that the Gaussian assumptions-free interpretable LDA is capable of indicating informative frequency bands and fault characteristic frequencies." @default.
• W4380091963 created "2023-06-10" @default.
• W4380091963 creator A5004755559 @default.
• W4380091963 creator A5046454314 @default.
• W4380091963 creator A5073881354 @default.
• W4380091963 creator A5088996662 @default.
• W4380091963 date "2023-09-01" @default.
• W4380091963 modified "2023-09-27" @default.
• W4380091963 title "Gaussian assumptions-free interpretable linear discriminant analysis for locating informative frequency bands for machine condition monitoring" @default.
• W4380091963 cites W1964511482 @default.
• W4380091963 cites W1984516393 @default.
• W4380091963 cites W1985110006 @default.
• W4380091963 cites W2019505419 @default.
• W4380091963 cites W2046674752 @default.
• W4380091963 cites W2049550263 @default.
• W4380091963 cites W2049978175 @default.
• W4380091963 cites W2056700360 @default.
• W4380091963 cites W2076601007 @default.
• W4380091963 cites W2086230222 @default.
• W4380091963 cites W2093849451 @default.
• W4380091963 cites W2437984376 @default.
• W4380091963 cites W2485614840 @default.
• W4380091963 cites W2751891979 @default.
• W4380091963 cites W2773549135 @default.
• W4380091963 cites W2789811186 @default.
• W4380091963 cites W2810292802 @default.
• W4380091963 cites W2810586164 @default.
• W4380091963 cites W2839815754 @default.
• W4380091963 cites W2896214849 @default.
• W4380091963 cites W2906578288 @default.
• W4380091963 cites W2969736276 @default.
• W4380091963 cites W2970444935 @default.
• W4380091963 cites W2989066261 @default.
• W4380091963 cites W2998506103 @default.
• W4380091963 cites W3006388671 @default.
• W4380091963 cites W3009370740 @default.
• W4380091963 cites W3038197494 @default.
• W4380091963 cites W3086626272 @default.
• W4380091963 cites W3094629563 @default.
• W4380091963 cites W3110138466 @default.
• W4380091963 cites W3122347867 @default.
• W4380091963 cites W3126725177 @default.
• W4380091963 cites W3157855421 @default.
• W4380091963 cites W3158410226 @default.
• W4380091963 cites W3198086451 @default.
• W4380091963 cites W3209651137 @default.
• W4380091963 cites W3215227219 @default.
• W4380091963 cites W4200616694 @default.
• W4380091963 cites W4226071416 @default.
• W4380091963 cites W4241570161 @default.
• W4380091963 cites W427289305 @default.
• W4380091963 cites W4308001098 @default.
• W4380091963 cites W4311086583 @default.
• W4380091963 cites W4320890101 @default.
• W4380091963 doi "https://doi.org/10.1016/j.ymssp.2023.110492" @default.
• W4380091963 hasPublicationYear "2023" @default.
• W4380091963 type Work @default.
• W4380091963 citedByCount "0" @default.
• W4380091963 crossrefType "journal-article" @default.
• W4380091963 hasAuthorship W4380091963A5004755559 @default.
• W4380091963 hasAuthorship W4380091963A5046454314 @default.
• W4380091963 hasAuthorship W4380091963A5073881354 @default.
• W4380091963 hasAuthorship W4380091963A5088996662 @default.
• W4380091963 hasConcept C104267543 @default.
• W4380091963 hasConcept C119599485 @default.
• W4380091963 hasConcept C119857082 @default.
• W4380091963 hasConcept C121332964 @default.
• W4380091963 hasConcept C127413603 @default.
• W4380091963 hasConcept C152745839 @default.
• W4380091963 hasConcept C153180895 @default.
• W4380091963 hasConcept C154945302 @default.
• W4380091963 hasConcept C163716315 @default.
• W4380091963 hasConcept C172707124 @default.
• W4380091963 hasConcept C19118579 @default.
• W4380091963 hasConcept C2775846686 @default.
• W4380091963 hasConcept C2776135515 @default.
• W4380091963 hasConcept C31972630 @default.
• W4380091963 hasConcept C33923547 @default.
• W4380091963 hasConcept C41008148 @default.
• W4380091963 hasConcept C61326573 @default.
• W4380091963 hasConcept C62520636 @default.
• W4380091963 hasConcept C69738355 @default.
• W4380091963 hasConcept C84462506 @default.
• W4380091963 hasConcept C9390403 @default.
• W4380091963 hasConceptScore W4380091963C104267543 @default.
• W4380091963 hasConceptScore W4380091963C119599485 @default.
• W4380091963 hasConceptScore W4380091963C119857082 @default.
• W4380091963 hasConceptScore W4380091963C121332964 @default.
• W4380091963 hasConceptScore W4380091963C127413603 @default.
• W4380091963 hasConceptScore W4380091963C152745839 @default.
• W4380091963 hasConceptScore W4380091963C153180895 @default.
• W4380091963 hasConceptScore W4380091963C154945302 @default.
• W4380091963 hasConceptScore W4380091963C163716315 @default.
• W4380091963 hasConceptScore W4380091963C172707124 @default.
• W4380091963 hasConceptScore W4380091963C19118579 @default.
• W4380091963 hasConceptScore W4380091963C2775846686 @default.
• W4380091963 hasConceptScore W4380091963C2776135515 @default.
• W4380091963 hasConceptScore W4380091963C31972630 @default.

• next