Abstract
One of the most critical assignments in fault diagnosis is to decide the finest set of features by evaluating the statistical parameters of the time-domain signals. However, these parameters are vulnerable under variable speed conditions, i.e., different loads, and speeds to capture the dynamic attributes of various health types. Therefore, this paper proposes a vibration imagining-based diagnosis approach for bearing under variable speed conditions. First, a Discrete Cosine Stockwell Transformation (DCST) coefficient-based preprocessing step is proposed to create an identical health pattern for variable speed conditions. Then, from that 2D coefficient matrix, a vibration image is created to capture those health patterns into grayscale. Finally, a Transfer Learning embedded Convolutional Neural Network (TL-CNN) is proposed to inspect the comprehensive structure of the 2D vibration images for final classification. The experimental results show that the proposed method achieved 100% classification accuracy on a publicly available dataset.
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Acknowledgments
This research was financially supported by the Ministry of Small and Medium-sized Enterprises (SMEs) and Startups (MSS), Korea, under the “Regional Specialized Industry Development Plus Program (R&D, S3092711)” supervised by the Korea Institute for Advancement of Technology (KIAT).
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Ahmad, Z., Hasan, M.J., Kim, JM. (2022). Transfer Learning with 2D Vibration Images for Fault Diagnosis of Bearings Under Variable Speed. In: Abraham, A., Gandhi, N., Hanne, T., Hong, TP., Nogueira Rios, T., Ding, W. (eds) Intelligent Systems Design and Applications. ISDA 2021. Lecture Notes in Networks and Systems, vol 418. Springer, Cham. https://doi.org/10.1007/978-3-030-96308-8_14
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