https://www.scipedia.com/wd/index.php?action=history&feed=atom&title=Sha_Qian_2026a 2026-04-06T01:02:29Z Revision history for this page on the wiki MediaWiki 1.27.0-wmf.10 https://www.scipedia.com/wd/index.php?title=Sha_Qian_2026a&diff=329309&oldid=prev 2026-01-27T09:44:23Z

<p>Scipediacontent moved page <a href="/public/Review_404818520741" class="mw-redirect" title="Review 404818520741">Review 404818520741</a> to <a href="/public/Sha_Qian_2026a" title="Sha Qian 2026a">Sha Qian 2026a</a></p> <table class="diff diff-contentalign-left" data-mw="interface"> <tr style='vertical-align: top;' lang='en'> <td colspan='1' style="background-color: white; color:black; text-align: center;">← Older revision</td> <td colspan='1' style="background-color: white; color:black; text-align: center;">Revision as of 09:44, 27 January 2026</td> </tr><tr><td colspan='2' style='text-align: center;' lang='en'><div class="mw-diff-empty">(No difference)</div> </td></tr></table>

Scipediacontent https://www.scipedia.com/wd/index.php?title=Sha_Qian_2026a&diff=329204&oldid=prev 2026-01-27T09:10:04Z

<p>JSanchez moved page <a href="/public/Draft_Sanchez_Pinedo_947099893" class="mw-redirect" title="Draft Sanchez Pinedo 947099893">Draft Sanchez Pinedo 947099893</a> to <a href="/public/Review_404818520741" class="mw-redirect" title="Review 404818520741">Review 404818520741</a></p> <table class="diff diff-contentalign-left" data-mw="interface"> <tr style='vertical-align: top;' lang='en'> <td colspan='1' style="background-color: white; color:black; text-align: center;">← Older revision</td> <td colspan='1' style="background-color: white; color:black; text-align: center;">Revision as of 09:10, 27 January 2026</td> </tr><tr><td colspan='2' style='text-align: center;' lang='en'><div class="mw-diff-empty">(No difference)</div> </td></tr></table>

JSanchez https://www.scipedia.com/wd/index.php?title=Sha_Qian_2026a&diff=329203&oldid=prev 2026-01-27T09:09:24Z

<p>Created page with &quot; == Abstract == &lt;p&gt;To overcome difficulties such as non-stationary vibrations, highdimensional feature redundancy, and mode selection issues that may arise during signal deco...&quot;</p> <p><b>New page</b></p><div><br /> == Abstract ==<br /> <br /> &lt;p&gt;To overcome difficulties such as non-stationary vibrations, highdimensional feature redundancy, and mode selection issues that may arise during signal decomposition in bearing fault diagnosis. We propose an adaptive method called Adaptive Variational Mode Decomposition (AVMD) for extracting time-frequency domain characteristics from the bearing vibration displacement signals to the maximum extent possible. Next, the ReliefF algorithm is employed to select desired features, and an autoencoder is used to reduce the selected features dimensionally. Furthermore, because the Hunter-Prey Optimisation (HPO) algorithm can balance multiple objectives during the search process by utilising the concepts of hunter and prey to generate a better solution set, incorporating this algorithm into the Deep Belief Network (DBN) establishes an HPO-DBN fault diagnosis model. Subsequently, we validate the proposed method using both public datasets and field compressor data. Moreover, we compare the results with those obtained from the Support Vector Machine (SVM). The findings indicate that this approach enhances the bearing fault identification rate, thus supporting predictive maintenance of bearings.OPEN ACCESS Received: 13/08/2025 Accepted: 16/10/2025 Published: 23/01/2026&lt;/p&gt;<br /> <br /> <br /> <br /> == Document ==<br /> &lt;pdf&gt;Media:Draft_Sanchez Pinedo_947099893-4391-document.pdf&lt;/pdf&gt;</div>

JSanchez