Improved Fault Detection Using Shifting Window Data Augmentation of Induction Motor Current Signals

Robert Wright, Poria Fajri (), Xingang Fu and Arash Asrari
Additional contact information
Robert Wright: Department of Electrical and Biomedical Engineering, University of Nevada, Reno, 1664 N Virginia St., Reno, NV 89557, USA
Poria Fajri: Department of Electrical and Biomedical Engineering, University of Nevada, Reno, 1664 N Virginia St., Reno, NV 89557, USA
Xingang Fu: Department of Electrical and Biomedical Engineering, University of Nevada, Reno, 1664 N Virginia St., Reno, NV 89557, USA
Arash Asrari: Department of Electrical and Computer Engineering, Purdue University Northwest, 2200 169th Street, Hammond, IN 46323, USA

Energies, 2024, vol. 17, issue 16, 1-19

Abstract: Deep learning models have demonstrated potential in Condition-Based Monitoring (CBM) for rotating machinery, such as induction motors (IMs). However, their performance is significantly influenced by the size of the training dataset and the way signals are presented to the model. When trained on segmented signals over a fixed period, the model’s accuracy can decline when tested on signals that differ from the training interval or are randomly sampled. Conversely, models utilizing data augmentation techniques exhibit better generalization to unseen conditions. This paper highlights the bias introduced by traditional training methods towards specific periodic waveform sampling and proposes a new method to augment phase current signals during training using a shifting window technique. This approach is considered as a practical approach for motor current augmentation and is shown to enhance classification accuracy and improved generalization when compared to existing techniques.

Keywords: deep learning; convolutional neural network; fault diagnosis; induction motor; short-time Fourier transform; data augmentation (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1996-1073/17/16/3956/pdf (application/pdf)
https://www.mdpi.com/1996-1073/17/16/3956/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:17:y:2024:i:16:p:3956-:d:1453350

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().