arXiv subjects

Thomas G. Habetler

Publications and source records attributed to Thomas G. Habetler.

5 recordsLinked to original sources

arXiv2021

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

The rapid development of artificial intelligence and deep learning has provided many opportunities to further enhance the safety, stability, and accuracy of industrial Cyber-Physical Systems (CPS). As indispensable components to many mission-critical CPS assets and equipment, mechanical bearings need to be monitored to identify any trace of abnormal conditions. 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. In many practical applications, however, it can be unsafe and time-consuming to collect sufficient data samples for each fault category, making it challenging to train a robust classifier. In this paper, we propose a few-shot learning framework for bearing fault diagnosis based on model-agnostic meta-learning (MAML), which targets for training an effective fault classifier using limited data. In addition, it can leverage the training data and learn to identify new fault scenarios more efficiently. Case studies on the generalization to new artificial faults show that the proposed framework achieves an overall accuracy up to 25% higher than a Siamese network-based benchmark study. Finally, the robustness and the generalization capability of the proposed framework are further validated by applying it to identify real bearing damages using data from artificial damages, which compares favorably against 6 state-of-the-art few-shot learning algorithms using consistent test environments.

cs.LG↗

arXiv2021

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

Interior permanent magnet synchronous machine drives are widely employed in electric traction systems and various industrial processes. However, prolonged exposure to high temperatures while operating can demagnetize the permanent magnets to the point of irreversible demagnetization. In addition, direct measurements with infrared sensors or contact-type sensors with wireless communication can be expensive and intrusive to the motor drive systems. This paper thus proposes a nonintrusive thermal monitoring scheme for the permanent magnets inside the direct-torque-controlled interior permanent magnet synchronous machines. By applying an external high-frequency rotating flux or torque signal to the hysteresis torque controller in the motor drive, the high-frequency currents can be injected into the stator windings. The permanent magnet temperature can thus be monitored based on the induced high-frequency resistance. The nonintrusive nature of the method is indicated by the elimination of the extra sensors and no hardware change to the existing system. Finally, the effectiveness of the proposed method is validated with experimental results.

eess.SY↗

arXiv2020

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

In this survey paper, we systematically summarize existing literature on bearing fault diagnostics with machine learning (ML) and data mining techniques. While conventional ML methods, including artificial neural network (ANN), principal component analysis (PCA), support vector machines (SVM), etc., have been successfully applied to the detection and categorization of bearing faults for decades, recent developments in deep learning (DL) algorithms in the last five years have sparked renewed interest in both industry and academia for intelligent machine health monitoring. 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. Specifically, the superiority of DL based methods over conventional ML methods are analyzed in terms of fault feature extraction and classification performances; many new functionalities enabled by DL techniques are also summarized. In addition, to obtain a more intuitive insight, a comparative study is conducted on the classification accuracy of different algorithms utilizing the open-source Case Western Reserve University (CWRU) bearing dataset. Finally, to facilitate the transition on applying various DL algorithms to bearing fault diagnostics, detailed recommendations and suggestions are provided for specific application conditions such as the setup environment, the data size, and the number of sensors and sensor types. Future research directions to further enhance the performance of DL algorithms on health monitoring are also discussed.

cs.LG↗

arXiv2019

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

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. In practical applications, however, obtaining accurate labels based on real-time bearing conditions can be far more challenging than simply collecting a huge amount of unlabeled data using various sensors. In this paper, we thus propose a semi-supervised learning approach for bearing anomaly detection using variational autoencoder (VAE) based deep generative models, which allows for effective utilization of dataset when only a small subset of data have labels. Finally, a series of experiments is performed using both the Case Western Reserve University (CWRU) bearing dataset and the University of Cincinnati's Center for Intelligent Maintenance Systems (IMS) dataset. The experimental results demonstrate that the proposed semi-supervised learning scheme greatly outperforms two mainstream semi-supervised learning approaches and a baseline supervised convolutional neural network approach, with the overall accuracy improvement ranging between 3% to 30% using different proportions of labeled samples.

cs.LG↗

arXiv2019

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

The optimization of electric machines at multiple operating points is crucial for applications that require frequent changes on speeds and loads, such as the electric vehicles, to strive for the machine optimal performance across the entire driving cycle. 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. Therefore, this paper proposes the utilization of t-distributed stochastic neighbor embedding (t-SNE) to visualize all of the optimization objectives of various electric machines design candidates with various operating conditions, which constitute a high-dimensional set of data that would lie on several different, but related, low-dimensional manifolds. Finally, two case studies of switched reluctance machines (SRM) are presented to illustrate the superiority of then t-SNE when compared to traditional visualization techniques used in electric machine optimizations.

cs.LG↗