no code implementations • 3 Jun 2021 • Shen Zhang, Sufei Li, Lijun He, Jose A. Restrepo, Thomas G. Habetler
This paper thus proposes a nonintrusive thermal monitoring scheme for the permanent magnets inside the direct-torque-controlled interior permanent magnet synchronous machines.
no code implementations • 25 Jul 2020 • Shen Zhang, Fei Ye, Bingnan Wang, Thomas G. Habetler
Most of the data-driven approaches applied to bearing fault diagnosis up-to-date are trained using a large amount of fault data collected a priori.
no code implementations • 2 Dec 2019 • Shen Zhang, Fei Ye, Bingnan Wang, Thomas G. Habetler
Most of the data-driven approaches applied to bearing fault diagnosis up to date are established in the supervised learning paradigm, which usually requires a large set of labeled data collected a priori.
no code implementations • 4 Nov 2019 • Shen Zhang, Shibo Zhang, Sufei Li, Liang Du, Thomas G. Habetler
However, the number of objectives that would need to be optimized would significantly increase with the number of operating points considered in the optimization, thus posting a potential problem in regards to the visualization techniques currently in use, such as in the scatter plots of Pareto fronts, the parallel coordinates, and in the principal component analysis (PCA), inhibiting their ability to provide machine designers with intuitive and informative visualizations of all of the design candidates and their ability to pick a few for further fine-tuning with performance verification.
no code implementations • 24 Jan 2019 • Shen Zhang, Shibo Zhang, Bingnan Wang, Thomas G. Habetler
In this paper, we first provide a brief review of conventional ML methods, before taking a deep dive into the state-of-the-art DL algorithms for bearing fault applications.