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

<div>The eigenvalues distribution of the covariance matrix <svg height="8.97279pt" id="M226" 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> of <svg height="9.39034pt" id="M227" style="vertical-align:-3.42943pt" version="1.1" viewbox="-0.0498162 -5.96091 7.65486 9.39034" width="7.65486pt" 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="M556 393C556 426 537 448 514 448C496 448 478 435 471 420C468 414 462 401 466 394C473 382 480 368 480 346C480 268 392 143 338 67H336C329 163 319 253 309 330S286 448 254 448C215 448 160 383 127 337L143 311C167 344 200 373 208 373S222 365 229 320C246 214 264 66 268 -20C219 -83 131 -171 17 -239L25 -261L137 -235C248 -110 273 -79 335 8C384 77 481 215 520 287C544 332 556 363 556 393Z" id="g113-122"></path></g></svg><i>.</i></div>

Shock and Vibration

fig5

Figure 5

Figure 5: Feature Frequency Extraction Based on Principal Component Analysis and Its Application in Axis Orbit 