Isolation and identification of rolling bearing compound faults based on adaptive periodized singular spectrum analysis and Rényi entropy

Li, Shengqiang; Yan, Changfeng; Hou, Yunfeng; Meng, Jiadong; Wen, Tao

doi:10.1088/1361-6501/ad2bca

Cited by 2 publications

(1 citation statement)

References 52 publications

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“…In recent years, scholars employed signal processing methods to explore single rolling element faults to improve the accuracy and efficiency of fault diagnosis by extracting fault characteristics from vibration signals [9][10][11][12][13][14]. Zhang et al [15] introduced windowed correlation kurtosis (WCK) method along with an improved wavelet transform for the timedomain identification of multi-rolling element faults.…”

Section: Introductionmentioning

confidence: 99%

Multi-rolling element faults diagnosis of rolling bearing based on time-frequency analysis and multi-curves extraction

Liu,

Yan,

et al. 2024

Meas. Sci. Technol.

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In industrial production, rolling bearings are widely used as key mechanical components in all types of rotating machinery. Fault diagnosis is essential for predicting bearing damage in advance, avoiding sudden equipment downtime and reducing economic losses. However, rolling element fault diagnosis of rolling bearings continues to be a challenge, especially with multi-rolling element faults. In view of the characteristics of randomness, weakness, and coupling in the vibration signal generated by multi-rolling element faults in rolling bearings, a multi-rolling element fault detection method is proposed by combination time-frequency (TF) analysis (TFA) with multi-curves extraction methods. The pre-processing method combined autoregressive model with maximum correlated kurtosis deconvolution is employed to enhance the weak periodic fault impulses in the raw vibration signals of the rolling bearing. Then an improved dynamic path multi-curves extraction method is proposed to extract multiple TF curves from the TF spectrogram (TFS) constructed via short-time Fourier transform. According to the proposed classification criteria, the TF curves are classified as homologous faults. The TF masking (TFM) method is employed to keep TF information closely associated with the fault impulse. Finally, the fault signals are reconstructed sequentially based on the TFS processed by TFM, and precise identification of multi-rolling element faults is achieved by envelope analysis. Experimental results demonstrate the effectiveness of the proposed method in extracting the weak fault features of multi-rolling elements and accomplishing fault separation and diagnosis.

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Section: Introductionmentioning

confidence: 99%

Multi-rolling element faults diagnosis of rolling bearing based on time-frequency analysis and multi-curves extraction

Liu,

Yan,

et al. 2024

Meas. Sci. Technol.

View full text Add to dashboard Cite

show abstract

A compound fault diagnosis method for rolling bearings based on the IPSO-MOMEDA and Teager energy operator

Li,

Yan,

Hou

et al. 2024

Meas. Sci. Technol.

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Different types of faults interact with each other and are easily overwhelmed by strong noise, which is challenging to identify and isolate single fault features in rolling bearing compound faults (RBCF). To address this problem, a diagnosis method for RBCF with Improved Particle Swarm Optimization Multipoint Optimal Minimum Entropy Deconvolution Adjusted (IPSO-MOMEDA) and Teager energy operator is proposed. The optimal settings for the period and filter length are automatically determined by excellent optimization capabilities of IPSO. Moreover, the optimal deconvolution of various fault types is obtained by optimized MOMEDA. Teager energy operator is introduced to enhance shock and periodicity components of deconvolution signal, which can ensure the accurate fault cycle can be selected by MOMEDA. Envelope analysis is employed for identification of compound fault characteristics. Both simulation and experimental results demonstrate that inner ring and outer ring faults, inner ring and ball faults, outer ring and ball faults, inner-outer ring and ball faults can be accurately diagnosed by the proposed method.

show abstract

Isolation and identification of rolling bearing compound faults based on adaptive periodized singular spectrum analysis and Rényi entropy

Cited by 2 publications

References 52 publications

Multi-rolling element faults diagnosis of rolling bearing based on time-frequency analysis and multi-curves extraction

Multi-rolling element faults diagnosis of rolling bearing based on time-frequency analysis and multi-curves extraction

A compound fault diagnosis method for rolling bearings based on the IPSO-MOMEDA and Teager energy operator

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