Reliable Fault Diagnosis of Rolling Bearing Based on Ensemble Modified Deep Metric Learning

<div>The network architecture. <i>X</i> is the input data sample of the network, <svg height="14.5263pt" id="M17" style="vertical-align:-2.268101pt" version="1.1" viewbox="-0.0498162 -12.2582 76.5734 14.5263" width="76.5734pt" 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="M300 -147C201 -63 143 98 143 270S200 602 300 686L282 710C136 610 70 450 70 271V270C70 89 136 -72 282 -170L300 -147Z"></path></g><g transform="matrix(.013,0,0,-0.013,4.498,0)"><path d="M499 88L487 113C459 86 422 65 413 65C403 65 403 74 409 98L461 318C487 426 460 448 431 448C408 448 383 441 352 424C305 398 223 344 157 257H155L243 674C249 702 249 712 240 712C227 712 170 680 87 673L82 648H117C155 648 162 644 150 590L23 -4L30 -12C55 -4 81 3 105 8C113 53 131 150 143 187C199 281 323 387 372 387C390 387 393 369 380 311L328 86C313 19 319 -12 347 -12C379 -12 442 24 499 88Z"></path></g><g transform="matrix(.0091,0,0,-0.0091,11.206,-5.741)"><path 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212 250L267 526H271L622 -8H655L732 396C747 477 762 532 770 556C785 600 798 616 862 619L870 650Z"></path></g><g transform="matrix(.0091,0,0,-0.0091,17.721,-5.741)"><path d="M283 271C283 451 220 610 70 710L48 683C146 603 207 443 207 271S149 -59 48 -142L70 -169C220 -67 283 90 283 271Z"></path></g></svg> is the output of the top layer. <svg height="12.4646pt" id="M19" style="vertical-align:-0.2063999pt" version="1.1" viewbox="-0.0498162 -12.2582 27.6295 12.4646" width="27.6295pt" 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="M962 650H739L732 622L760 619C812 613 819 604 798 552C760 457 671 267 606 131H604L511 638H480L237 131H233L183 554C177 607 183 614 226 619L248 622L257 650H24L17 622C88 615 92 611 103 524L173 -11H203L450 491H453L543 -11H575L839 529C882 609 886 613 953 622L962 650Z"></path></g><g transform="matrix(.0091,0,0,-0.0091,12.669,-5.741)"><path d="M309 -142C211 -61 151 99 151 271S210 601 309 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transform="matrix(.0091,0,0,-0.0091,17.114,-5.741)"><path d="M283 271C283 451 220 610 70 710L48 683C146 603 207 443 207 271S149 -59 48 -142L70 -169C220 -67 283 90 283 271Z"></path></g></svg> are the parameters of the network to be learned, 1 ≤ <i>n</i> ≤ <i>N</i>.</div>

Shock and Vibration

fig2

Figure 2

Figure 2: Reliable Fault Diagnosis of Rolling Bearing Based on Ensemble Modified Deep Metric Learning 