Pages that link to "Item:Q406125"
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The following pages link to Bearing fault diagnosis based on multiscale permutation entropy and support vector machine (Q406125):
Displaying 14 items.
- Time series analysis using composite multiscale entropy (Q742686) (← links)
- Multiple feature vectors based fault classification for WSN integrated bearing of rolling mill (Q1629491) (← links)
- Improved similarity-based modeling for the classification of rotating-machine failures (Q1661468) (← links)
- Mechanical fault diagnosis using color image recognition of vibration spectrogram based on quaternion invariable moment (Q1666381) (← links)
- A comparative study on apen, sampen and their fuzzy counterparts in a multiscale framework for feature extraction (Q1956440) (← links)
- Rolling element bearing diagnosis based on probability box theory (Q1989019) (← links)
- Multivariate multiscale entropy of financial markets (Q2007428) (← links)
- Bearing fault diagnosis with kernel sparse representation classification based on adaptive local iterative filtering-enhanced multiscale entropy features (Q2298789) (← links)
- Fault diagnosis and prognosis of bearing based on hidden Markov model with multi-features (Q2690683) (← links)
- Rolling bearing fault classification based on envelope spectrum and support vector machine (Q2852405) (← links)
- Intelligent diagnosis of fan fault based on evidence theory and support vector machine (Q3180617) (← links)
- Bearing Fault Diagnosis Based on Hilbert Marginal Spectrum and Supervised Locally Linear Embedding (Q5377633) (← links)
- Advances in Neural Networks – ISNN 2005 (Q5707641) (← links)
- A Summary: Quantifying the Complexity of Financial Markets Using Composite and Multivariate Multiscale Entropy (Q5855892) (← links)
