YYÜ GCRIS Basic veritabanının içerik oluşturulması ve kurulumu Research Ecosystems (https://www.researchecosystems.com) tarafından devam etmektedir. Bu süreçte gördüğünüz verilerde eksikler olabilir.
 

Distributed Bearing Fault Classification of Induction Motors Using 2-D Deep Learning Model

No Thumbnail Available

Date

2024

Journal Title

Journal ISSN

Volume Title

Publisher

Ieee-inst Electrical Electronics Engineers inc

Abstract

Distributed bearing faults are highly common in industrial applications and display unpredictable vibration patterns impeding their detection. These faults stem from issues such as lubrication deficiencies, contamination, electrical erosion, roughness of the bearing surface, or the propagation of localized faults. This study aims to detect distributed bearing faults by utilizing a multisensory approach consisting of current, accelerometer, and fluxgate sensors. A novel 2-D deep learning framework is proposed, leveraging signals from six distinct sources, including three-axis vibration signals, stray magnetic flux signal, and two-phase current signals. Data are collected from 3- and 10-hp induction motors at 50 operational points, spanning ten speed levels and five torque levels. These six signals are transformed into matrices and combined to create a comprehensive matrix that provides an overall depiction of the bearing condition. The proposed deep learning architecture employs a 2-D convolutional model, which takes 2-D images as input and determines the bearing status. To evaluate the system's robustness, the data are divided into training and testing sets. The proposed model demonstrates remarkable effectiveness in detecting distributed bearing faults, achieving an impressive accuracy rate of 99.92%. Furthermore, a comprehensive comparison is provided, highlighting the impact of using various sets of inputs as sources for the deep learning model on the accuracy rate for each set. Through the analysis of the obtained results, a clear conclusion can be drawn: the model performs at its best when all six input sources are utilized.

Description

Rajabioun, Ramin/0000-0003-3972-6442; Afshar, Mojtaba/0000-0002-6173-0440

Keywords

Feature Extraction, Induction Motors, Fault Diagnosis, Vibrations, Dc Motors, Brushless Dc Motors, Indexes, Deep Learning (Dl), Distributed Bearing Fault Detection, Distributed Fault Detection, 2-D Convolutional Neural Network (Cnn)

Turkish CoHE Thesis Center URL

WoS Q

N/A

Scopus Q

N/A

Source

Volume

5

Issue

1

Start Page

115

End Page

125