The Hybrid KICA-GDA-LSSVM Method Research on Rolling Bearing Fault Feature Extraction and Classification

<table class="figure-group"><tr class="fig-image" id="a"><td><object data="https://static.hindawi.com/articles/sv/volume-2015/512163/figures/512163.fig.0013a.svgz" name="512163.fig.0013a" type="image/svg+xml"></object></td></tr><tr class="fig-caption"><td><b>(a) </b>Features processed by KICA</td></tr><tr class="fig-image" id="b"><td><object data="https://static.hindawi.com/articles/sv/volume-2015/512163/figures/512163.fig.0013b.svgz" name="512163.fig.0013b" type="image/svg+xml"></object></td></tr><tr class="fig-caption"><td><b>(b) </b>Features processed by KICA-GDA</td></tr></table>

<div>Projected feature space plots.</div>

Shock and Vibration

fig13

Figure 13

Figure 13: The Hybrid KICA-GDA-LSSVM Method Research on Rolling Bearing Fault Feature Extraction and Classification 