Train Wheelset Bearing Multifault Impulsive Component Separation Using Hierarchical Shift-Invariant Dictionary Learning

<div>Selection of the number of iterations <svg height="12.2532pt" id="M77" style="vertical-align:-3.29108pt" version="1.1" viewbox="-0.0498162 -8.96212 12.249 12.2532" width="12.249pt" 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="M620 675H597C578 656 570 650 541 650H144C112 650 104 653 94 675H72C59 618 42 552 23 493L53 491C71 534 88 564 105 585C124 608 144 615 238 615H290L197 121C182 40 174 34 88 28L82 0H361L367 28C275 34 266 38 281 121L374 615H441C522 615 543 608 553 583C562 560 566 531 565 493L597 494C603 551 612 629 620 675Z"></path></g><g transform="matrix(.0091,0,0,-0.0091,7.176,3.132)"><path d="M245 635C92 635 37 457 37 312C37 149 91 -12 244 -12C395 -12 449 166 449 312C449 469 395 635 245 635ZM243 598C332 598 358 454 358 312C358 173 334 26 245 26C158 26 128 174 128 313S152 598 243 598Z"></path></g></svg> based on SES: (a) SES values for the different number of iterations; (b) extracted impulsive components.</div>

Shock and Vibration

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Figure 3

Figure 3: Train Wheelset Bearing Multifault Impulsive Component Separation Using Hierarchical Shift-Invariant Dictionary Learning 