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Bearing fault diagnosis using multidomain fusion-based vibration imaging and multitask learning.

Hasan, Md. Junayed; Islam, M. M. Manjurul; Kim, Jong-Myon


M. M. Manjurul Islam

Jong-Myon Kim


Statistical features extraction from bearing fault signals requires a substantial level of knowledge and domain expertise. Furthermore, existing feature extraction techniques are mostly confined to selective feature extraction methods namely, time-domain, frequency-domain, or time-frequency domain statistical parameters. Vibration signals of bearing fault are highly non-linear and non-stationary making it cumbersome to extract relevant information for existing methodologies. This process even became more complicated when the bearing operates at variable speeds and load conditions. To address these challenges, this study develops an autonomous diagnostic system that combines signal-to-image transformation techniques for multi-domain information with convolutional neural network (CNN)-aided multitask learning (MTL). To address variable operating conditions, a composite color image is created by fusing information from multi-domains, such as the raw time-domain signal, the spectrum of the time-domain signal, and the envelope spectrum of the time-frequency analysis. This 2-D composite image, named multi-domain fusion-based vibration imaging (MDFVI), is highly effective in generating a unique pattern even with variable speeds and loads. Following that, these MDFVI images are fed to the proposed MTL-based CNN architecture to identify faults in variable speed and health conditions concurrently. The proposed method is tested on two benchmark datasets from the bearing experiment. The experimental results suggested that the proposed method outperformed state-of-the-arts in both datasets.


HASAN, M.J., ISLAM, M.M.M. and KIM, J.-M. 2022. Bearing fault diagnosis using multidomain fusion-based vibration imaging and multitask learning. Sensors [online], 22(1): sensing technologies for fault diagnostics and prognosis, article 56. Available from:

Journal Article Type Article
Acceptance Date Dec 20, 2021
Online Publication Date Dec 22, 2021
Publication Date Jan 1, 2022
Deposit Date May 26, 2022
Publicly Available Date May 26, 2022
Journal Sensors
Print ISSN 1424-8220
Publisher MDPI
Peer Reviewed Peer Reviewed
Volume 22
Issue 1
Article Number 56
Keywords Electrical and Electronic Engineering; Biochemistry; Instrumentation; Atomic and Molecular Physics, and Optics; Analytical Chemistry
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