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Enhancement of bearing fault detection using an alternative analytic energy operator and sparse Bayesian step-filtering

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Abstract

The bearing fault signal is a kind of weak signal, so it is easy to be submerged by background noise. As such, signature extraction is facing a great challenge; hence, an effective signature extraction method plays an essential role in bearing fault extraction. In this paper, a new method for bearing fault detection based on an alternative analytic energy operator and sparse Bayesian step-filtering (SBSF) was applied. The SBSF technique can remove much background noise from the raw signal and enhance the characteristics related to the bearing fault. Besides, it has also a high calculation efficiency. Afterward, an improved analytic energy operator, the symmetric high-order analytic energy operator (SHO-AEO), which is an enhanced demodulation technique that outperforms the conventional demodulation technique, was applied to detect bearing fault signatures from filtered signals. The proposed energy measure is formed using the original signal, its Hilbert transform, and its high-order derivatives. Unlike traditional energy operators, it includes the information of the real and imaginary parts of the analytic signal. As a demodulation technique, it is also tailored to extract both the amplitude and frequency modulations from the filtered signal. Furthermore, compared with the previous energy operators, it provides better anti-noise capability. Hence, the proposed fault detection method of combining the SHO-AEO and SBSF not only has high computational efficiency but also provides much better noise handling potential. Through simulated and real tests, this proposed method is demonstrated to be robust against various noise levels and to detect the bearing fault signature.

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Acknowledgments

This work was supported by the National Natural Science Foundation of China under Grant 51675399. The authors thank Machinery Failure Prevention Technology Society and Case Western Reserve University for contributing the vibration data freely.

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Authors and Affiliations

  1. State Engineering Laboratory of Highway Maintenance Equipment, Chang’an University, Xi’an, Shaanxi, 710064, China

    Yan Wang & Lichen Gu

  2. Institute of Electromechanical Technology, Xi’an University of Architecture and Technology, Xi’an, 710055, China

    Lichen Gu

  3. School of Automation, Xi’an University of Posts and Telecommunications, Xi’an, Shaanxi, 710121, China

    Yuanbo Xu

Authors
  1. Yan Wang
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  2. Lichen Gu
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  3. Yuanbo Xu
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Corresponding author

Correspondence to Lichen Gu.

Additional information

Recommended by Editor No-cheol Park

Yan Wang received the B.S. and the M.S. in Mechatronics Engineering from Chang’an University, Xi’an, China, respectively, in 2010 and 2013. She is currently studying for her doctorate at the School of Engineering Machinery of Chang ‘an University, China. Her research interests include mechanical condition monitoring and fault diagnosis, hydraulic transmission, and control.

Lichen Gu received the B.S. in Mining Machinery Engineering and the M.S. in Structural Mechanics from Xi’an University of Architecture and Technology, Xi’an, China, in 1978 and 1989, respectively. He received a Ph.D. in Mechatronics Engineering from Xi’an Jiaotong University, Xi’an, China, in 2002. He is currently a Full Professor of Mechanical Engineering at Xi’an University of Architecture and Technology and also a part-time professor at State Engineering Laboratory of Highway Maintenance Equipment, Chang’an University, Xi’an, China. His research interests include dynamic measurement and fault diagnosis for hydraulic equipment, hydraulic transmission and control, and mechatronic design.

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Wang, Y., Gu, L. & Xu, Y. Enhancement of bearing fault detection using an alternative analytic energy operator and sparse Bayesian step-filtering. J Mech Sci Technol 35, 905–920 (2021). https://doi.org/10.1007/s12206-021-0204-1

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  • DOI: https://doi.org/10.1007/s12206-021-0204-1

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