Feature Frequency Extraction Based on Principal Component Analysis and Its Application in Axis Orbit

<div>The eigenvalues distribution of covariance matrix <span class="nowrap"><svg height="8.97279pt" id="M240" style="vertical-align:-0.2324896pt" version="1.1" viewbox="-0.0498162 -8.7403 8.92059 8.97279" width="8.92059pt" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink"><g transform="matrix(.013,0,0,-0.013,0,0)"><path d="M625 186C574 85 512 32 429 32C293 32 194 153 194 337C194 485 274 621 419 621C496 621 573 586 606 468L643 475C636 537 629 583 620 638C593 644 519 665 438 665C199 665 42 523 42 317C42 158 152 -15 418 -15C486 -15 578 5 604 11C621 46 647 122 660 171L625 186Z" id="g13-65"></path></g></svg>.</span> (a) The eigenvalues distribution of D11. (b) The eigenvalues distribution of D12.</div>

Shock and Vibration

fig15

Figure 15

Figure 15: Feature Frequency Extraction Based on Principal Component Analysis and Its Application in Axis Orbit 