Fault Diagnosis of Rotating Machinery Based on One-Dimensional Deep Residual Shrinkage Network with a Wide Convolution Layer

<table class="table-group" id="tab4"><tr><td><table class="table"><tr><td class="thead-hr" colspan="6"><hr/></td></tr><tr class="thead"><td class="align_left">The number of RBUs</td><td class="align_center">1 (%)</td><td class="align_center">2</td><td class="align_center">3</td><td class="align_center">4</td><td class="align_center">5 (%)</td></tr><tr><td class="thead-hr" colspan="6"><hr/></td></tr><tr><td class="align_left">Accuracy</td><td class="align_center">87.84</td><td class="align_center">94.44%</td><td class="align_center">92.37%</td><td class="align_center">93.17</td><td class="align_center">93.84</td></tr><tr class="table-tr"><td colspan="6"><hr class="tbody-hr"/></td></tr></table></td></tr></table>

<div>Influence of the number of RBUs on the 1D-WDRSN model.</div>

Shock and Vibration

tab4

Table 4

Table 4: Fault Diagnosis of Rotating Machinery Based on One-Dimensional Deep Residual Shrinkage Network with a Wide Convolution Layer 