Using a Support Vector Machine Based Decision Stage to Improve the Fault Diagnosis on Gearboxes

<table class="table-group" id="tab1"><tr><td><table class="table"><tr><td class="thead-hr" colspan="2"><hr/></td></tr><tr class="thead"><td class="align_left">Component</td><td class="align_center">Description</td></tr><tr><td class="thead-hr" colspan="2"><hr/></td></tr><tr><td class="align_left">Electric motor (M)</td><td class="align_center">Motor Siemens 1LA7 090-4YA60 1 : 49 kW, 4 poles, and 28 : 33 Hz</td></tr><tr><td class="align_left">Gear 1 (Z1)</td><td class="align_center">Pinion: 76 mm, 30 teeth, pressure angle = 20, and helix angle = 20</td></tr><tr><td class="align_left">Gear 2 (Z2)</td><td class="align_center">Gear: 112 mm, 45 teeth, pressure angle = 20, and helix angle = 20</td></tr><tr><td class="align_left">Brake (B)</td><td class="align_center">Magnetic brake: proportional force to input voltage and belt coupled</td></tr><tr class="table-tr"><td colspan="2"><hr class="tbody-hr"/></td></tr></table></td></tr></table>

<div>Characteristics of the components of the experimental arrangement.</div>

Computational Intelligence and Neuroscience

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Table 1

Table 1: Using a Support Vector Machine Based Decision Stage to Improve the Fault Diagnosis on Gearboxes 