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A parameter-adaptive ACMD method based on particle swarm optimization algorithm for rolling bearing fault diagnosis under variable speed

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Abstract

Fault diagnosis for rolling bearing under variable speed is always a challenging topic since the vibration signal has time-varying characteristics. To overcome this difficulty, a novel method is exploited based on particle swarm optimization (PSO) and adaptive chirp mode decomposition (ACMD), named parameter-adaptive ACMD. Firstly, fast spectral kurtosis algorithm is used to get the resonance band signal. Then, the parameter-adaptive ACMD method decomposes the envelope signal to obtain the time-frequency spectrum. Next, the proposed method of fault diagnosis uses peak search algorithm to estimate instantaneous rotational frequency from the time-frequency graph processed. Finally, the measured rotational frequency is used as the phase function in the resampling process to get the order spectrum and fault characteristic order (FCO). Simulation and actual signals were analyzed and the results indicate that the proposed method can identify fault type with variable speed and has high application value.

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Acknowledgments

This work is supported by the National Natural Science Foundation of China (11790282), the General program of National Natural Science Foundation of China (12072207), the Hebei Province 333 Talents Project (A201802004), the 2020 independent project of State Key Laboratory of Mechanical Behavior and System Safety of Traffic Engineering Structures (ZZ2020-39), and Graduate Innovation Funding Project (YC2020062).

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Authors and Affiliations

  1. State Key Laboratory of Mechanical Behavior and System Safety of Traffic Engineering Structures, Shijiazhuang Tiedao University, Shijiazhuang, 050043, China

    Zengqiang Ma, Suyan Liu & Xin Li

  2. School of Electrical and Electronic Engineering, Shijiazhuang Tiedao University, Shijiazhuang, 050043, China

    Zengqiang Ma, Feiyu Lu & Suyan Liu

Authors
  1. Zengqiang Ma
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  2. Feiyu Lu
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  3. Suyan Liu
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  4. Xin Li
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Corresponding author

Correspondence to Suyan Liu.

Additional information

Zengqiang Ma received his Ph.D. from Beijing Jiaotong University, China, in 2011. Now he is a Doctoral Supervisor and the Deputy Dean of the School of Electrical and Electronic Engineering at Shijiazhuang Tiedao University. He mainly researches the field of safety operation state monitoring and fault diagnosis of rail vehicles.

Feiyu Lu received his B.S. in 2018 from Bei Hua University, now he is pursuing his M.S. at Shijiazhuang Tiedao University. His main research interests include mechanical signal processing, rotating machinery condition monitoring and fault diagnosis.

Suyan Liu received her Ph.D. from the Beijing University of Posts and Telecommunications, China, in 2019. Now she is a lecturer at School of Shijiazhuang Tiedao University, China. Her current research interests include machine learning, Internet of Things, multimedia image processing technology for fault diagnosis of rolling bearing.

Xin Li received her B.S. in 2012 from Liaoning Shihua University, her M.S. in 2014 from Northeastern University, now is a Ph.D. candidate at Shijiazhuang Tiedao University. Her main research interests include rotating machinery condition monitoring and fault diagnosis.

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Ma, Z., Lu, F., Liu, S. et al. A parameter-adaptive ACMD method based on particle swarm optimization algorithm for rolling bearing fault diagnosis under variable speed. J Mech Sci Technol 35, 1851–1865 (2021). https://doi.org/10.1007/s12206-021-0405-7

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  • DOI: https://doi.org/10.1007/s12206-021-0405-7

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