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

<div>Eigenvalues distribution of covariance matrix <svg height="8.97279pt" id="M119" 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> with two frequency components.</div>

Shock and Vibration

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Figure 2

Figure 2: Feature Frequency Extraction Based on Principal Component Analysis and Its Application in Axis Orbit 