Random Radial Basis Function Kernel-based Support Vector Machine

2021 ◽  
Author(s):  
Xiaojian Ding ◽  
Jian Liu ◽  
Fan Yang ◽  
Jie Cao
Keyword(s):  
Support Vector Machine ◽  
Radial Basis Function ◽  
Basis Function ◽  
Support Vector ◽  
Radial Basis Function Kernel ◽  
Radial Basis

Adaptive data-driven nonlinear synchro squeezed transform with single class radial basis function kernel support vector machine applied to wind turbine planetary gearbox fault diagnostics

2019 ◽  
Vol 234 (7) ◽  
pp. 1015-1025
Author(s):  
R Uma Maheswari ◽  
R Umamaheswari
Keyword(s):  
Support Vector Machine ◽  
High Resolution ◽  
Radial Basis Function ◽  
Amplitude Modulation ◽  
Basis Function ◽  
Support Vector ◽  
Radial Basis Function Kernel ◽  
Single Class ◽  
Kernel Support Vector Machine ◽  
Radial Basis

Planetary stage gears operated at low rotational speed and varying wind speed result variation in load. Variable speed and variable load induce nonstationary operating conditions. Vibration signal measured from Wind power gear transmission systems are embedded with multiple sources of vibration and attenuated considerably as it travels from source of vibration to measuring point. Efficacious multi-component decomposition without mode mixing ensures the accurate fault signature recognition. Synchro squeezing transform is the promising tool that represents the ridges with high resolution in time as well as in frequency axis. An efficient vibration analysis technique, short windowed Fourier synchro squeezing transform with nonlinear radial basis function kernel support vector machine is proposed to detect the mechanical faults in low speed planetary stage of wind turbines. Raw vibration is modeled in time–frequency plane to extract fault pattern signatures effectively with high resolution by adapting an empirical nonlinear tool synchro squeezing transforms. Amplitude modulation and frequency modulation parameters are sculpted from instantaneous amplitude and instantaneous phase, frequency. Hybrid feature space with signal attributes, statistical moments, and randomness measures are extricated from amplitude modulation-frequency modulation components. Single class radial basis function support vector machine is trained with hybrid features. The fault detection accuracy of the proposed method is compared with the standard variants of empirical mode decomposition. The proposed short windowed Fourier synchro squeezing transform-radial basis function kernel support vector machine shows 98.2% accuracy, 98% sensitivity, and 98% specificity.

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