Matches in SemOpenAlex for { <https://semopenalex.org/work/W4384284166> ?p ?o ?g. }
- W4384284166 endingPage "19634" @default.
- W4384284166 startingPage "19623" @default.
- W4384284166 abstract "Most health indicators used to predict bearing performance degradation are calculated through supervised learning of failure data. However, due to the limited availability of failure data during the operation of bearings and the uncertainty of failure occurrences, using a supervised learning method to predict failure thresholds of bearings becomes challenging. In this study, a bearing degradation model is proposed through the development of health indicators using unsupervised learning. The proposed method extracts features from data with high signal amplitudes among multi-time windows data through frequency analysis and develops a health indicator (HI) by fusing these features using principal component analysis (PCA). The six-sigma value for the bearing’s HI data is used as a threshold for determining the first predicted time to failure (FPTF), which is a criterion for an unsupervised method of detecting abnormalities in the performance bearings in real-time. The proposed method accurately detects abnormal states and the degradation model shows improved trendability compared to existing models." @default.
- W4384284166 created "2023-07-15" @default.
- W4384284166 creator A5002227636 @default.
- W4384284166 creator A5030146723 @default.
- W4384284166 creator A5074375633 @default.
- W4384284166 date "2023-09-01" @default.
- W4384284166 modified "2023-10-16" @default.
- W4384284166 title "Bearing Fault Degradation Modeling Based on Multi-time Windows Fusion Unsupervised Health Indicator" @default.
- W4384284166 cites W1986907389 @default.
- W4384284166 cites W2005523062 @default.
- W4384284166 cites W2014685668 @default.
- W4384284166 cites W2064323378 @default.
- W4384284166 cites W2373436644 @default.
- W4384284166 cites W2523377413 @default.
- W4384284166 cites W2531135300 @default.
- W4384284166 cites W2587865582 @default.
- W4384284166 cites W2594845301 @default.
- W4384284166 cites W2766688321 @default.
- W4384284166 cites W2776641310 @default.
- W4384284166 cites W2782623557 @default.
- W4384284166 cites W2889139199 @default.
- W4384284166 cites W2899275444 @default.
- W4384284166 cites W2899717669 @default.
- W4384284166 cites W2904284137 @default.
- W4384284166 cites W2904460913 @default.
- W4384284166 cites W2940935128 @default.
- W4384284166 cites W2942721092 @default.
- W4384284166 cites W2958041981 @default.
- W4384284166 cites W2961333734 @default.
- W4384284166 cites W3007685460 @default.
- W4384284166 cites W3016665419 @default.
- W4384284166 cites W3087922815 @default.
- W4384284166 cites W3094110601 @default.
- W4384284166 cites W3132921282 @default.
- W4384284166 cites W3134412914 @default.
- W4384284166 cites W3152133971 @default.
- W4384284166 cites W3158937816 @default.
- W4384284166 cites W3163734136 @default.
- W4384284166 cites W3167992366 @default.
- W4384284166 cites W3193451403 @default.
- W4384284166 cites W3201907027 @default.
- W4384284166 cites W3203050329 @default.
- W4384284166 cites W4200445429 @default.
- W4384284166 cites W4211206835 @default.
- W4384284166 cites W4229063675 @default.
- W4384284166 cites W4283765700 @default.
- W4384284166 cites W4289823422 @default.
- W4384284166 cites W4292622317 @default.
- W4384284166 cites W4315796995 @default.
- W4384284166 cites W4367146864 @default.
- W4384284166 cites W620261401 @default.
- W4384284166 doi "https://doi.org/10.1109/jsen.2023.3294361" @default.
- W4384284166 hasPublicationYear "2023" @default.
- W4384284166 type Work @default.
- W4384284166 citedByCount "0" @default.
- W4384284166 crossrefType "journal-article" @default.
- W4384284166 hasAuthorship W4384284166A5002227636 @default.
- W4384284166 hasAuthorship W4384284166A5030146723 @default.
- W4384284166 hasAuthorship W4384284166A5074375633 @default.
- W4384284166 hasConcept C119599485 @default.
- W4384284166 hasConcept C119857082 @default.
- W4384284166 hasConcept C124101348 @default.
- W4384284166 hasConcept C127313418 @default.
- W4384284166 hasConcept C127413603 @default.
- W4384284166 hasConcept C153180895 @default.
- W4384284166 hasConcept C154945302 @default.
- W4384284166 hasConcept C165205528 @default.
- W4384284166 hasConcept C175551986 @default.
- W4384284166 hasConcept C199978012 @default.
- W4384284166 hasConcept C27438332 @default.
- W4384284166 hasConcept C2775846686 @default.
- W4384284166 hasConcept C2779679103 @default.
- W4384284166 hasConcept C41008148 @default.
- W4384284166 hasConcept C52622490 @default.
- W4384284166 hasConcept C76155785 @default.
- W4384284166 hasConcept C8038995 @default.
- W4384284166 hasConceptScore W4384284166C119599485 @default.
- W4384284166 hasConceptScore W4384284166C119857082 @default.
- W4384284166 hasConceptScore W4384284166C124101348 @default.
- W4384284166 hasConceptScore W4384284166C127313418 @default.
- W4384284166 hasConceptScore W4384284166C127413603 @default.
- W4384284166 hasConceptScore W4384284166C153180895 @default.
- W4384284166 hasConceptScore W4384284166C154945302 @default.
- W4384284166 hasConceptScore W4384284166C165205528 @default.
- W4384284166 hasConceptScore W4384284166C175551986 @default.
- W4384284166 hasConceptScore W4384284166C199978012 @default.
- W4384284166 hasConceptScore W4384284166C27438332 @default.
- W4384284166 hasConceptScore W4384284166C2775846686 @default.
- W4384284166 hasConceptScore W4384284166C2779679103 @default.
- W4384284166 hasConceptScore W4384284166C41008148 @default.
- W4384284166 hasConceptScore W4384284166C52622490 @default.
- W4384284166 hasConceptScore W4384284166C76155785 @default.
- W4384284166 hasConceptScore W4384284166C8038995 @default.
- W4384284166 hasFunder F4320322120 @default.
- W4384284166 hasFunder F4320335199 @default.
- W4384284166 hasIssue "17" @default.
- W4384284166 hasLocation W43842841661 @default.
- W4384284166 hasOpenAccess W4384284166 @default.