Pages that link to "Item:Q4638020"
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The following pages link to Rolling bearing fault diagnosis and health assessment using EEMD and the adjustment Mahalanobis–Taguchi system (Q4638020):
Displaying 9 items.
- Bearing condition monitoring based on shock pulse method and improved redundant lifting scheme (Q960329) (← links)
- Multiple feature vectors based fault classification for WSN integrated bearing of rolling mill (Q1629491) (← links)
- An enhanced diagnosis method for weak fault features of bearing acoustic emission signal based on compressed sensing (Q1981093) (← links)
- Modeling of the safe region based on support vector data description for health assessment of wheelset bearings (Q1984923) (← links)
- An improved ABC algorithm and its application in bearing fault diagnosis with EEMD (Q2003322) (← links)
- An unsupervised fault diagnosis method for rolling bearing using STFT and generative neural networks (Q2198631) (← links)
- EMD and GNN-adaboost fault diagnosis for urban rail train rolling bearings (Q2321715) (← links)
- Unsupervised cross-domain rolling bearing fault diagnosis based on time-frequency information fusion (Q2681938) (← links)
- Kolmogorov-Smirnov test for rolling bearing performance degradation assessment and prognosis (Q2846346) (← links)
