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Weak fault feature extraction of rolling bearing under strong poisson noise and variable speed conditions

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Abstract

Fault feature extraction of the rolling bearing under strong background noise is always a difficult problem in bearing fault diagnosis. At present, most of the research focuses on weak signal extraction under Gaussian white noise and has certain practical significance. However, the noise in engineering is often complex and changeable, Gaussian white noise cannot fully simulate the actual strong background noise. Poisson white noise is a type of typical non-Gaussian noise, which widely exists in complex mechanical impact. It is of great significance to study the weak fault feature extraction of a faulty bearing under this type of noise. At the same time, variable speed conditions occupy most rotating machinery speed conditions. Non-stationary vibration signals make it difficult to extract fault features, and the frequency spectrum ambiguity will occur because of speed fluctuation. To solve the above problems, a method of weak feature extraction of a faulty bearing based on computed order analysis (COA) and adaptive stochastic resonance (SR) is proposed. Firstly, by numerical simulation, the non-stationary fault characteristic signal corrupted with strong Poisson noise is transformed into a stationary signal in the angle domain by COA. Secondly, the influence of the parameters of the pulse arrival rate and noise intensity of Poisson white noise on the optimal SR response in the angle domain are studied, and the influence of the parameters of Poisson white noise on the fault feature extraction is given. Then, adaptive SR method is used to extract and enhance fault feature information. Finally, the effectiveness of this method in weak fault characteristic signal extraction under strong Poisson noise is verified by experiments. Numerical simulation and experimental results verify the effectiveness of the proposed method in bearing fault diagnosis under strong Poisson noise and variable speed conditions.

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Acknowledgments

This project is supported by the Major Special Basic Research Projects of Aeroengine and Gas Turbine (Grant No. 2017-IV-0008-0045), China.

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Authors and Affiliations

  1. Department of Mechanics, Tianjin University, Tianjin, 300354, China

    Qiang Ma & Shuqian Cao

  2. College of Mechanical and Equipment Engineering, Hebei University of Engineering, Handan, 056038, China

    Qiang Ma

  3. Key Laboratory of Intelligent Industrial Equipment Technology of Hebei Province (Hebei University of Engineering), Handan, 056038, China

    Qiang Ma

  4. Jiangsu Key Laboratory of Mine Mechanical and Electrical Equipment, School of Mechatronic Engineering, China University of Mining and Technology, Xuzhou, 221116, China

    Tao Gong & Jianhua Yang

Authors
  1. Qiang Ma
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  2. Shuqian Cao
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  3. Tao Gong
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  4. Jianhua Yang
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Corresponding author

Correspondence to Shuqian Cao.

Additional information

Qiang Ma received the B.S. degree in Mechanical and Electrical Engineering from Hebei Institute of Architecture and Technology in 2002, and a Master’s degree in Industrial Engineering from Hebei University of Engineering in 2010. He is currently pursuing a Doctorate degree in the School of Mechanical Engineering of Tianjin University since 2018. He has been an Associate Professor of Mechanical Engineering at Hebei University of Engineering, Handan City, Hebei province, China. He has published more than 10 papers in journals sponsored by IEEE, SAGE, ASME and other academic institutions. His main research interests include R&D and manufacturing of mechanical and electrical equipment, intelligent monitoring and fault diagnosis of mechanical systems.

Shuqian Cao who received his Ph.D. in Dynamics and Control from Tianjin University, is currently a Professor of Mechanics in the School of Mechanical Engineering, Tianjin University. He is now the Director of Tianjin Key Laboratory of Nonlinear Dynamics and Control, Council Member of Chinese Society for Vibration Engineering (CSVE). His research effort is focused on machine and structure dynamics, especially on the theory and applications of nonlinear dynamics in rotary machines, covering the fields of aero-engines, gas turbines and combustion engines. He and his co-workers have published over 100 papers and 3 books.

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Ma, Q., Cao, S., Gong, T. et al. Weak fault feature extraction of rolling bearing under strong poisson noise and variable speed conditions. J Mech Sci Technol 36, 5341–5351 (2022). https://doi.org/10.1007/s12206-022-1001-1

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  • DOI: https://doi.org/10.1007/s12206-022-1001-1

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