首页|Data on Support Vector Machines Described by a Researcher at Shanghai Maritime University (Rolling bearing fault diagnosis based on multi-domain features and whale optimized support vector ma- chine)
Data on Support Vector Machines Described by a Researcher at Shanghai Maritime University (Rolling bearing fault diagnosis based on multi-domain features and whale optimized support vector ma- chine)
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NETL
NSTL
Sage
New study results on have been published. According to news reporting out of Shanghai, People's Republic of China, by NewsRx editors, research stated, "Rolling bearing is an important rotating support component in mechanical equipment." Financial supporters for this research include Shanghai Natural Science Foundation of China; China Postdoctoral Science Foundation; Shanghai Engineering Technology Research Center Construction Projects; National High-tech Research And Development Program; National Natural Science Foundation of China. The news reporters obtained a quote from the research from Shanghai Maritime University: "It is very prone to wear, defects, and other faults, which directly affect the reliable operation of mechanical equipment. Its running condition monitoring and fault diagnosis have always been a matter of concern to engineers and researchers. A rolling bearing fault diagnosis technique based on multi-domain feature and whale optimization algorithm-support vector machine (MDF-WOA-SVM) is proposed. Firstly, recursive analysis is performed on vibration signal and the recursive features are employed as nonlinear recursive fea- ture vector including recursive rate (RR), deterministic rate (DET), recursive entropy (RE), and diagonal average length (DAL). Then, a comprehensive multi-domain feature vector is constructed by combining three time-domain features including root mean square, variance, and peak to peak. Finally, whale opti- mization algorithm (WOA) is introduced to optimize the penalty factor C and kernel function parameter g to construct the optimal WOA-SVM model."
NETL
NSTL
Sage
