Pages that link to "Item:Q2287470"
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The following pages link to Degradation trend prediction for rotating machinery using long-range dependence and particle filter approach (Q2287470):
Displaying 7 items.
- Predicting remaining useful life of rotating machinery based artificial neural network (Q611424) (← links)
- Performance prediction methodology based on pattern recognition (Q634035) (← links)
- Fast prediction with sparse multikernel LS-SVR using multiple relevant time series and its application in avionics system (Q1665732) (← links)
- Performance degradation prediction for a hydraulic servo system based on Elman network observer and GMM-SVR (Q2282699) (← links)
- A novel multi-information fusion grey model and its application in wear trend prediction of wind turbines (Q2310677) (← links)
- Kolmogorov-Smirnov test for rolling bearing performance degradation assessment and prognosis (Q2846346) (← links)
- Neuro-fuzzy based condition prediction of bearing health (Q2852397) (← links)
