Search Results for author: Thomas G. Habetler

Found 5 papers, 0 papers with code

Rotor Thermal Monitoring Scheme for Direct-Torque-Controlled Interior Permanent Magnet Synchronous Machines via High-Frequency Rotating Flux or Torque Injection

no code implementations3 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.

Few-Shot Bearing Fault Diagnosis Based on Model-Agnostic Meta-Learning

no code implementations25 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.

Anomaly Detection Few-Shot Learning

Semi-Supervised Learning of Bearing Anomaly Detection via Deep Variational Autoencoders

no code implementations2 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.

Anomaly Detection

Visualization of Multi-Objective Switched Reluctance Machine Optimization at Multiple Operating Conditions with t-SNE

no code implementations4 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.

Machine Learning and Deep Learning Algorithms for Bearing Fault Diagnostics -- A Comprehensive Review

no code implementations24 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.

BIG-bench Machine Learning Time Series Analysis

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