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<h1 id="yifei-ding">Yifei Ding (丁逸飞)</h1>
<p><img src="http://120.55.102.104:9000/oaimages/yifei_ding.jpg" alt="yifei_ding" style="zoom:33%;" /></p>
<p><img src="https://orcid.org/assets/icons/favicon.ico" /><a href="https://orcid.org/0000-0001-8408-6945">ORCID</a>     <img src="https://www.researchgate.net/favicon-96x96.png" style="zoom: 18%;" /><a href="https://www.researchgate.net/profile/Yifei-Ding-3">ResearchGate</a></p>
<h2 id="about-me"><strong>About me</strong></h2>
<p><strong>PhD student</strong> (Apr 2020 ~ Today)</p>
<p><strong>Languages</strong></p>
<p>Chinese; English</p>
<p><strong>Research interest</strong></p>
<p>Equipment status monitoring and fault diagnosis, prognostic and health management for rotating machinery, deep learning, transfer learning, and dynamic signal processing.</p>
<h2 id="affiliation"><strong>Affiliation</strong></h2>
<p><a href="http://www.rccmfd.com/">Research Center of Condition Monitoring and Fault Diagnosis (RCCMFD)</a></p>
<p><img src="http://120.55.102.104:9000/oaimages/seu_color.jpg" style="zoom:5%;" /> <a href="https://www.seu.edu.cn/">Southeast University (SEU)</a></p>
<p>Supervisor: Prof. Minping Jia</p>
<h2 id="address"><strong>Address</strong></h2>
<pre><code>School of Mechanical Engineering
Southeast University (Jiulonghu Campus)
2 Southeast University, Nanjing, Jiangsu 211189
P. R. China
</code></pre>
<h2 id="preprint"><strong>Preprint</strong></h2>
<ul>
<li>Y. Ding, M. Jia, Q. Miao, and Y. Cao, "A novel Time-frequency Transformer and its Application in Fault Diagnosis of Rolling Bearings." <em>arXiv</em>:<a href="https://arxiv.org/abs/2104.09079">2104.09079</a>, Apr. 2021.</li>
</ul>
<h2 id="publications"><strong>Publications</strong></h2>
<p><strong><em>Prognostic and Health Management</em></strong> <em>(my current research topics)</em></p>
<ul>
<li><u>Y. Ding</u>, M. Jia, and Y. Cao, “Remaining Useful Life Estimation Under Multiple Operating Conditions via Deep Subdomain Adaptation,” <em>IEEE Trans. Instrum. Meas.</em>, vol. 70, pp. 1–11, 2021, doi: <a href="https://www.doi.org/10.1109/TIM.2021.3076567">10.1109/TIM.2021.3076567</a>.</li>
<li><u>Y. Ding</u>, M. Jia, Q. Miao, and P. Huang, “Remaining useful life estimation using deep metric transfer learning for kernel regression,” <em>Reliab. Eng. Syst. Saf.</em>, vol. 212, Aug. 2021, doi: <a href="https://www.doi.org/10.1016/j.ress.2021.107583">10.1016/j.ress.2021.107583</a>.</li>
<li><u>Y. Ding</u>, P. Ding, and M. Jia, “A Novel Remaining Useful Life Prediction Method of Rolling Bearings Based on Deep Transfer Auto-Encoder,” <em>IEEE Trans. Instrum. Meas.</em>, vol. 70, pp. 1–12, 2021, doi: <a href="https://www.doi.org/10.1109/TIM.2021.3072670">10.1109/TIM.2021.3072670</a>.</li>
<li>Y. Cao, <u>Y. Ding</u>, M. Jia, and R. Tian, “A novel temporal convolutional network with residual self-attention mechanism for remaining useful life prediction of rolling bearings,” <em>Reliab. Eng. Syst. Saf.</em>, vol. 215, p. 107813, Nov. 2021, doi: <a href="https://www.doi.org/10.1016/j.ress.2021.107813">10.1016/j.ress.2021.107813</a>.</li>
<li>P. Ding, M. Jia, <u>Y. Ding</u>, and X. Zhao, “Statistical Alignment-Based Metagated Recurrent Unit for Cross-Domain Machinery Degradation Trend Prognostics Using Limited Data,” <em>IEEE Trans. Instrum. Meas.</em>, vol. 70, pp. 1–12, 2021, doi: <a href="https://www.doi.org/10.1109/TIM.2021.3056644">10.1109/TIM.2021.3056644</a>.</li>
<li>J. Zhuang, M. Jia, <u>Y. Ding</u>, and P. Ding, “Temporal convolution-based transferable cross-domain adaptation approach for remaining useful life estimation under variable failure behaviors,” <em>Reliab. Eng. Syst. Saf.</em>, vol. 216, p. 107946, Dec. 2021, doi: <a href="https://www.doi.org/10.1016/j.ress.2021.107946">10.1016/j.ress.2021.107946</a>.</li>
<li>Y. Cao, M. Jia, P. Ding, and <u>Y. Ding</u>, “Transfer learning for remaining useful life prediction of multi-conditions bearings based on bidirectional-GRU network,” <em>Measurement</em>, vol. 178, p. 109287, Jun. 2021, doi: <a href="https://www.doi.org/10.1016/j.measurement.2021.109287">10.1016/j.measurement.2021.109287</a>.</li>
</ul>
<p><strong><em>Other topics</em></strong> <em>(research before PhD)</em></p>
<ul>
<li>P. Huang, <u>Y. Ding</u>, Q. Miao, G. Sang, and M. Jia, “An improved contact detection algorithm for bonded particles based on multi-level grid and bounding box in DEM simulation,” <em>Powder Technol.</em>, vol. 374, pp. 577–596, Sep. 2020, doi: <a href="https://www.doi.org/10.1016/j.powtec.2020.07.022">10.1016/j.powtec.2020.07.022</a>.</li>
<li>P. Huang, <u>Y. Ding</u>, L. Wu, S. Fu, and M. Jia, “A novel approach of evaluating crushing energy in ball mills using regional total energy,” <em>Powder Technol.</em>, vol. 355, pp. 289–299, 2019, doi: <a href="https://www.doi.org/10.1016/j.powtec.2019.07.050">10.1016/j.powtec.2019.07.050</a>.</li>
<li>P. Huang, Y. Zhou, <u>Y. Ding</u>, Q. Miao, M. Jia, and L. Ma, “A novel method for particle cluster modeling based on internal force,” <em>Powder Technol.</em>, vol. 385, pp. 317–326, Mar. 2021, doi: <a href="https://www.doi.org/10.1016/j.powtec.2021.02.078">10.1016/j.powtec.2021.02.078</a>.</li>
<li>P. Huang, Q. Miao, <u>Y. Ding</u>, G. Sang, and M. Jia, “Research on surface segregation and overall segregation of particles in a rotating drum based on stacked image,” <em>Powder Technol.</em>, vol. 382, pp. 162–172, Apr. 2021, doi: <a href="https://www.doi.org/10.1016/j.powtec.2020.12.063">10.1016/j.powtec.2020.12.063</a>.</li>
<li>P. Huang, G. Sang, Q. Miao, <u>Y. Ding</u>, and M. Jia, “Soft measurement of ball mill load based on multi-classifier ensemble modelling and multi-sensor fusion with improved evidence combination,” <em>Meas. Sci. Technol.</em>, vol. 32, no. 1, p. 015105, Jan. 2020, doi: <a href="https://www.doi.org/10.1088/1361-6501/aba885">10.1088/1361-6501/aba885</a>.</li>
</ul>
<hr />
<p><em>(Updated Oct 1, 2021)</em></p>
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