Attention Mechanism-Based CNN-LSTM Model for Wind Turbine Fault Prediction Using SSN Ontology Annotation

<table class="table-group" id="tab6"><tr><td><table class="table"><tr><td class="thead-hr" colspan="2"><hr/></td></tr><tr class="thead"><td class="align_left">Features</td><td class="align_center">Score</td></tr><tr><td class="thead-hr" colspan="2"><hr/></td></tr><tr><td class="align_left">AI_NAC WindError</td><td class="align_center">0.324536</td></tr><tr><td class="align_left">AI_GBX_OilSumpTemp</td><td class="align_center">0.188854</td></tr><tr><td class="align_left">AI_NAC_Position</td><td class="align_center">0.102554</td></tr><tr><td class="align_left">C_15Min_Aver_WindSpeed</td><td class="align_center">0.082901</td></tr><tr><td class="align_left">AI_TBN_RotorSpeed</td><td class="align_center">0.072518</td></tr><tr><td class="align_left">AI_NAC_AirTemp</td><td class="align_center">0.055106</td></tr><tr><td class="align_left">AI_NAC_OutAirTemp</td><td class="align_center">0.053102</td></tr><tr><td class="align_left">C_TBN_10MinAveWindDir</td><td class="align_center">0.049441</td></tr><tr><td class="align_left">AI_YAW_Speed</td><td class="align_center">0.018063</td></tr><tr><td class="align_left">C_10Min_Aver_WindSpeed</td><td class="align_center">0.016823</td></tr><tr class="table-tr"><td colspan="2"><hr class="tbody-hr"/></td></tr></table></td></tr></table>

<div>Characteristics of wind turbine yaw fault after screening.</div>

Wireless Communications and Mobile Computing

tab6

Table 6

Table 6: Attention Mechanism-Based CNN-LSTM Model for Wind Turbine Fault Prediction Using SSN Ontology Annotation 