A Fatigue Life Prediction Method for the Drive System of Wind Turbine Using Internet of Things

<table class="table-group" id="tab2"><tr><td><table class="table"><tr><td class="thead-hr" colspan="4"><hr/></td></tr><tr><td class="align_left">Number of principal components retained (%)</td><td class="align_center">95</td><td class="align_center">97</td><td class="align_center">99</td></tr><tr><td class="align_left">Root mean square error</td><td class="align_center">3.17</td><td class="align_center">4.23</td><td class="align_center">5.24</td></tr><tr><td class="align_left">Mean absolute error</td><td class="align_center">3.07</td><td class="align_center">3.48</td><td class="align_center">4.35</td></tr><tr class="table-tr"><td colspan="4"><hr class="tbody-hr"/></td></tr></table></td></tr></table>

<div>Influence of feature selection steps on prediction results.</div>

Advances in Materials Science and Engineering

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Table 2

Table 2: A Fatigue Life Prediction Method for the Drive System of Wind Turbine Using Internet of Things 