Stator current analysis by subspace methods for fault detection in induction machines

Youness Trachi, Elhoussin Elbouchikhi, Vincent Choqueuse, Mohamed Benbouzid
<span title="">2015</span> <i title="IEEE"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/asvxkn74mje4tiafu5qy4idvoy" style="color: black;">IECON 2015 - 41st Annual Conference of the IEEE Industrial Electronics Society</a> </i> &nbsp;
This paper aims to develop a condition monitoring architecture for induction machines, with focus on bearing faults. The main objective of this paper is to identify fault signatures at an early stage by using high-resolution frequency estimation techniques. In particular, we present two subspace methods, which are Root-MUSIC and ESPRIT. Once the frequencies are determined, the amplitude estimation is obtained by using the Least Squares Estimator (LSE). Finally, the amplitude estimation is used
more &raquo; ... o derive a fault severity criterion. The experimental results show that the proposed architecture has the ability to measure the faults severity.
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/iecon.2015.7392639">doi:10.1109/iecon.2015.7392639</a> <a target="_blank" rel="external noopener" href="https://dblp.org/rec/conf/iecon/TrachiECB15.html">dblp:conf/iecon/TrachiECB15</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/aihct6gfy5ecfdty2wras7x3ma">fatcat:aihct6gfy5ecfdty2wras7x3ma</a> </span>
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