Fault diagnosis of rolling element bearing from vibration signal using one dimensional convolutional neural networks

Name: Supasin Sinkittiyanont

Organization : Chulalongkorn University. Department of Mechanical Engineering

Email : 6472087021@student.chula.ac.th

Name: Chanat Ratanasumawong

Organization : Chulalongkorn University. Department of Mechanical Engineering

Email : chanat.r@chula.ac.th

keyword: Rolling contact bearings

ThaSH: Fault location (Engineering)

LCSH: Vibration -- Measurement

ThaSH: Neural networks (Computer science)

Abstract: Failures of rolling element bearings can precipitate serious downtime. To ensure operational reliability in rotating machinery, it is necessary to diagnose the bearing failures accurately. The essential part of effective fault diagnosis lies in the detection of periodic impulses in the vibration signal. These impulses are characteristic of local faults and are directly related to the defect location in the bearing. Vibration analysis is widely employed to diagnose rolling bearing faults in condition monitoring. In this paper, a One-Dimensional Convolutional Neural Network (1D-CNN) was proposed for automatic feature extraction in the bearing fault diagnosis. The vibration dataset used here is from the Case Western Reserve University (CWRU) Bearing Data Center. The raw vibration data is augmented before input into the network. Both time and frequency domain signals were used as the inputs for feature learning. The results showed that the 1D-CNN model can extract and classify bearing faults effectively. The average maximum accuracies of the results from the time domain input and frequency domain input were 98.97% and 99.75%, respectively. It can be concluded that the frequency domain input is superior in capturing anomalies in the bearing vibration signal.

King Mongkut's University of Technology North Bangkok. Central Library

Address: BANGKOK

Email: library@kmutnb.ac.th

Created: 2024

Modified: 2025-05-28

Issued: 2025-05-28

บทความ/Article

application/pdf

BibliograpyCitation : In IEEE Thailand Section (IEEE Computer Society Thailand Chapter) and Prince of Songkla University. College of Computing. The 21st International Joint Conference on Computer Science and Software Engineering (JCSSE 2024)) (pp.390-397). Phuket : Prince of Songkla University

eng

©copyrights King Mongkut's University of Technology North Bangkok

ลำดับที่.	ชื่อแฟ้มข้อมูล	ขนาดแฟ้มข้อมูล	จำนวนเข้าถึง	วัน-เวลาเข้าถึงล่าสุด
1	JCSSE 2024pp.390-397.pdf	1.04 MB

ใช้เวลา

0.019816 วินาที