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Synchroextracting Transform and Deep Residual Network for Varying Speed Bearing Fault Diagnostic

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Journal of Vibration Engineering & Technologies Aims and scope Submit manuscript

Abstract

Background

Condition monitoring (CM) data obtained from practical applications often demonstrate non-stationary characteristics attributing to changing speed or/and loading conditions. CM data can also be affected by noise contamination as a result of machine operating and background noise. This then poses a challenge in the development of an operative technique for practical machine fault diagnostic.

Method

Aiming to address this issue, this study presents an automated fault diagnosis technique for varying speed bearings using Synchroextracting Transform (SET) and deep residual network (DRN). In this approach, SET is utilized to process the non-stationary signals to yield the desired time–frequency representations of the bearing data. The time–frequency graphs are then served as the feature set in the DRN network for training and fault classification.

Results

The classification result based on an experimental bearing dataset shows that the proposed technique can yield a highly accurate result for fault diagnosis of varying speed bearings. Gaussian white noise having a signal-to-noise ratio ranging from 0 to − 8 dB is also added onto the bearing CM data in separate simulations to evaluate the robustness of the proposed technique in dealing with industrial data.

Conclusion

The diagnostic result shows that the technique presented in this study can pertain a satisfying accuracy when the bearing signals are tainted by strong background noise.

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Acknowledgements

Financial supports from Qingdao Municipal Government through the Qingdao innovative leadership program (181219zhc) and the 111 project from the Ministry of Science and Technology of China (D21017) for this work are gratefully appreciated.

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Authors and Affiliations

  1. School of Mechanical and Automotive Engineering, Qingdao University of Technology, 777 Jialingjiang Road, West Coast New District, Qingdao, 266520, People’s Republic of China

    Kang Xi Sang, Jie Shang & Tian Ran Lin

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  1. Kang Xi Sang
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  2. Jie Shang
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  3. Tian Ran Lin
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Correspondence to Tian Ran Lin.

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On behalf of all contributing authors, I declare that there is no conflict of interest for this work. Tian Ran Lin: The corresponding author, and on the behalf of all authors.

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Part of the materials included in this manuscript was presented in APARM 2020 conference, 20–23, August 2020, Vancouver, Canada.

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Sang, K.X., Shang, J. & Lin, T.R. Synchroextracting Transform and Deep Residual Network for Varying Speed Bearing Fault Diagnostic. J. Vib. Eng. Technol. 11, 343–353 (2023). https://doi.org/10.1007/s42417-022-00584-y

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