Open Access

ARTICLE

A Novel Method Based on UNET for Bearing Fault Diagnosis

by Dileep Kumar^1,*, Imtiaz Hussain Kalwar², Tanweer Hussain¹, Bhawani Shankar Chowdhry¹, Sanaullah Mehran Ujjan¹, Tayab Din Memon³

1 National Centre of Robotics and Automation, HHCMS Lab, Mehran University of Engineering & Technology, Jamshoro, 76020, Sindh, Pakistan
2 Department of Electrical Engineering, DHA SUFFA University, Karachi, Sindh, Pakistan
3 Department of Electronic Engineering, Mehran University of Engineering and Technology, Jamshoro, 76020, Sindh, Pakistan

* Corresponding Author: Dileep Kumar. Email:

(This article belongs to the Special Issue: Machine Learning for Data Analytics)

Computers, Materials & Continua 2021, 69(1), 393-408. https://doi.org/10.32604/cmc.2021.014941

Received 28 October 2020; Accepted 25 February 2021; Issue published 04 June 2021

Abstract

Reliability of rotating machines is highly dependent on the smooth rolling of bearings. Thus, it is very essential for reliable operation of rotating machines to monitor the working condition of bearings using suitable fault diagnosis and condition monitoring approach. In the recent past, Deep Learning (DL) has become applicable in condition monitoring of rotating machines owing to its performance. This paper proposes a novel bearing fault diagnosis method based on the processing and analysis of the vibration images. The proposed method is the UNET model that is a recent development in DL models. The model is applied to the 2D vibration images obtained by transforming normalized amplitudes of the time-series vibration data samples into the corresponding vibration images. The UNET model performs pixel-level feature learning using the vibration images owing to its unique architecture. The results demonstrate that the model can perform dense predictions without any loss of label information, generally caused by the sliding window labelling method. The comparative analysis with other DL models confirmed the superiority of the UNET model which has achieved maximum accuracy of 98.91% and F1-Score of 99%.

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