Matches in SemOpenAlex for { <https://semopenalex.org/work/W3167917945> ?p ?o ?g. }
- W3167917945 endingPage "3929" @default.
- W3167917945 startingPage "3929" @default.
- W3167917945 abstract "This study discusses convolutional neural networks (CNNs) for vibration signals analysis, including applications in machining surface roughness estimation, bearing faults diagnosis, and tool wear detection. The one-dimensional CNNs (1DCNN) and two-dimensional CNNs (2DCNN) are applied for regression and classification applications using different types of inputs, e.g., raw signals, and time-frequency spectra images by short time Fourier transform. In the application of regression and the estimation of machining surface roughness, the 1DCNN is utilized and the corresponding CNN structure (hyper parameters) optimization is proposed by using uniform experimental design (UED), neural network, multiple regression, and particle swarm optimization. It demonstrates the effectiveness of the proposed approach to obtain a structure with better performance. In applications of classification, bearing faults and tool wear classification are carried out by vibration signals analysis and CNN. Finally, the experimental results are shown to demonstrate the effectiveness and performance of our approach." @default.
- W3167917945 created "2021-06-22" @default.
- W3167917945 creator A5025400748 @default.
- W3167917945 creator A5074214984 @default.
- W3167917945 date "2021-06-07" @default.
- W3167917945 modified "2023-10-10" @default.
- W3167917945 title "Deep Learning Approach for Vibration Signals Applications" @default.
- W3167917945 cites W1130761448 @default.
- W3167917945 cites W1964671517 @default.
- W3167917945 cites W2029255497 @default.
- W3167917945 cites W2045956898 @default.
- W3167917945 cites W2050323089 @default.
- W3167917945 cites W2056601507 @default.
- W3167917945 cites W2059518577 @default.
- W3167917945 cites W2076842831 @default.
- W3167917945 cites W2081097942 @default.
- W3167917945 cites W2112796928 @default.
- W3167917945 cites W2120390927 @default.
- W3167917945 cites W2145487065 @default.
- W3167917945 cites W2210061839 @default.
- W3167917945 cites W243674440 @default.
- W3167917945 cites W2521656013 @default.
- W3167917945 cites W2593479727 @default.
- W3167917945 cites W2607416494 @default.
- W3167917945 cites W2778801251 @default.
- W3167917945 cites W2796044624 @default.
- W3167917945 cites W2800567156 @default.
- W3167917945 cites W2802877483 @default.
- W3167917945 cites W280292566 @default.
- W3167917945 cites W2809350318 @default.
- W3167917945 cites W2895933188 @default.
- W3167917945 cites W2901389654 @default.
- W3167917945 cites W2906995570 @default.
- W3167917945 cites W2909430258 @default.
- W3167917945 cites W2909784400 @default.
- W3167917945 cites W2913855435 @default.
- W3167917945 cites W2917014261 @default.
- W3167917945 cites W2937927999 @default.
- W3167917945 cites W2946629017 @default.
- W3167917945 cites W2952181634 @default.
- W3167917945 cites W2953987170 @default.
- W3167917945 cites W2955506857 @default.
- W3167917945 cites W2965813890 @default.
- W3167917945 cites W2966008650 @default.
- W3167917945 cites W2967781381 @default.
- W3167917945 cites W2968906743 @default.
- W3167917945 cites W2969482699 @default.
- W3167917945 cites W2971396385 @default.
- W3167917945 cites W2971605905 @default.
- W3167917945 cites W2973096226 @default.
- W3167917945 cites W2976594877 @default.
- W3167917945 cites W2987709164 @default.
- W3167917945 cites W2988142396 @default.
- W3167917945 cites W2997308049 @default.
- W3167917945 cites W2998932403 @default.
- W3167917945 cites W3011704391 @default.
- W3167917945 cites W3036988671 @default.
- W3167917945 cites W3039300052 @default.
- W3167917945 cites W3090238656 @default.
- W3167917945 cites W3123409499 @default.
- W3167917945 cites W3158118773 @default.
- W3167917945 doi "https://doi.org/10.3390/s21113929" @default.
- W3167917945 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/8201341" @default.
- W3167917945 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/34200400" @default.
- W3167917945 hasPublicationYear "2021" @default.
- W3167917945 type Work @default.
- W3167917945 sameAs 3167917945 @default.
- W3167917945 citedByCount "19" @default.
- W3167917945 countsByYear W31679179452021 @default.
- W3167917945 countsByYear W31679179452022 @default.
- W3167917945 countsByYear W31679179452023 @default.
- W3167917945 crossrefType "journal-article" @default.
- W3167917945 hasAuthorship W3167917945A5025400748 @default.
- W3167917945 hasAuthorship W3167917945A5074214984 @default.
- W3167917945 hasBestOaLocation W31679179451 @default.
- W3167917945 hasConcept C102519508 @default.
- W3167917945 hasConcept C107365816 @default.
- W3167917945 hasConcept C119857082 @default.
- W3167917945 hasConcept C121332964 @default.
- W3167917945 hasConcept C127413603 @default.
- W3167917945 hasConcept C134306372 @default.
- W3167917945 hasConcept C153180895 @default.
- W3167917945 hasConcept C154945302 @default.
- W3167917945 hasConcept C159985019 @default.
- W3167917945 hasConcept C192562407 @default.
- W3167917945 hasConcept C198394728 @default.
- W3167917945 hasConcept C199978012 @default.
- W3167917945 hasConcept C24890656 @default.
- W3167917945 hasConcept C2776450708 @default.
- W3167917945 hasConcept C33923547 @default.
- W3167917945 hasConcept C41008148 @default.
- W3167917945 hasConcept C50644808 @default.
- W3167917945 hasConcept C523214423 @default.
- W3167917945 hasConcept C78519656 @default.
- W3167917945 hasConcept C81363708 @default.
- W3167917945 hasConcept C85617194 @default.
- W3167917945 hasConceptScore W3167917945C102519508 @default.
- W3167917945 hasConceptScore W3167917945C107365816 @default.