Dynamic modelling of deep groove ball bearings with different local defects considering skidding and thermal elastohydrodynamic lubrication

Self Cite

Due to the coupling of multiple fault feature information and contamination of heavy background noise, it is a challenging task to accurately identify Rolling Bearing Compound Fault (RBCF). A method for isolating and identifying the RBCF is proposed by integrating the Adaptive Periodized Singular Spectrum Analysis (APSSA) with Rényi entropy (RE). The adaptive selection of embedding dimension of Hankel matrix in APSSA without setting parameters empirically is proposed, and a selection criterion for singular values is established to preprocess the vibration signals of rolling bearing and enhance fault periodic component. A RE-based threshold value is introduced to further isolate and decouple the impulse segments of vibration signal in the time domain. Considering inner raceway fault, outer raceway fault, ball fault and skidding, a comprehensive simulation model of compound fault is constructed by the response mechanism of different excited resources. Simulated and experimental data are applied to validate the effectiveness and practicability of proposed method. The results demonstrate that RBCF can be identified correctly by the proposed method under strong background noise.

Section: Introductionmentioning

confidence: 99%

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

Li,

Yan,

Hou

et al. 2024

Self Cite

“…The investigation of rolling element fault diagnosis has its origins in the construction of vibration models for faults in rolling bearings [5]. The academics developed a dynamic model for defects in single rolling elements and explored the impact of rolling element faults on the vibration response of bearings [6][7][8]. The inherent mechanism of multi-rolling element faults is intricate, bringing the establishment of precise dynamic models challenging.…”

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

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.

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

Li,

Yan,

Hou

et al. 2024

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.