Rolling Bearing Diagnosing Method Based on Time Domain Analysis and Adaptive Fuzzy <svg xmlns:xlink="http://www.w3.org/1999/xlink" xmlns="http://www.w3.org/2000/svg" style="vertical-align:-0.2723999pt" id="M1" height="11.6718pt" version="1.1" viewBox="-0.0657574 -11.3994 11.6374 11.6718" width="11.6374pt"><g transform="matrix(.017,0,0,-0.017,0,0)"><path id="g113-68" d="M645 631C614 643 545 666 457 666C215 666 23 519 23 283C23 90 158 -16 337 -16C412 -16 489 2 522 10C543 39 590 127 606 167L580 181C519 89 459 18 348 18C201 18 122 136 122 287C122 464 244 632 435 632C544 632 602 595 608 472L639 475C643 526 645 581 645 631Z"/></g></svg>-Means Clustering

<div>The test stand (bearing data center of CWRU).</div>

Shock and Vibration

fig2

Figure 2

Figure 2: Rolling Bearing Diagnosing Method Based on Time Domain Analysis and Adaptive Fuzzy <svg xmlns:xlink="http://www.w3.org/1999/xlink" xmlns="http://www.w3.org/2000/svg" style="vertical-align:-0.2723999pt" id="M1" height="11.6718pt" version="1.1" viewBox="-0.0657574 -11.3994 11.6374 11.6718" width="11.6374pt"><g transform="matrix(.017,0,0,-0.017,0,0)"><path id="g113-68" d="M645 631C614 643 545 666 457 666C215 666 23 519 23 283C23 90 158 -16 337 -16C412 -16 489 2 522 10C543 39 590 127 606 167L580 181C519 89 459 18 348 18C201 18 122 136 122 287C122 464 244 632 435 632C544 632 602 595 608 472L639 475C643 526 645 581 645 631Z"/></g></svg>-Means Clustering 

Shock and Vibration

Rolling Bearing Diagnosing Method Based on Time Domain Analysis and Adaptive Fuzzy -Means Clustering

Figure 2