Reliability Assessment of Permanent Magnet Synchronous Motor Rolling Bearings Considering Long-Range Dependence
Niu Feng1,2, Sang Yuhao1,2, Zhao Changdong1,2, Xiang Shihu1,2, Chen Jianlin1,2
1. State Key Laboratory of Smart Power Distribution Equipment and System Hebei University of Technology Tianjin 300401 China;
2. School of Electrical Engineering Hebei University of Technology Tianjin 300401 China
Rolling bearing failures are a primary cause of malfunctions in permanent magnet synchronous motors (PMSMs), impairing operational stability and safety. Bearing degradation processes exhibit long-range dependence (LRD), meaning increments over non-overlapping time intervals are non-independent, while traditional Wiener process degradation models rely on an independent increment assumption and thus fail to characterize LRD. Additionally, conventional bearing degradation feature extraction methods depend on vibration signals, requiring extra accelerometers and limiting engineering applicability. To address these issues, an accelerated degradation model for PMSM rolling bearings based on stator current features and accounting for LRD is proposed to improve the accuracy of bearing reliability assessment.
Firstly, degradation features are directly extracted from PMSM stator current signals, eliminating the need for additional sensors and improving the engineering feasibility of bearing condition monitoring. Secondly, fractional Brownian motion (FBM) is adopted to characterize the LRD inherent in the bearing degradation process, and an inverse power law acceleration model is used to correlate degradation rate with stress level, which allows model parameter extrapolation from accelerated stress conditions to normal operating conditions. A linear mapping is established between vibration and current features as well, enabling the degradation model to be formulated based on current features while retaining the LRD property of the original degradation process. Thirdly, maximum likelihood estimation is applied for model parameter identification, with a genetic algorithm utilized to solve the global optimization problem caused by the complex structure of the likelihood function. Finally, the weak convergence theorem and space-time transformation are combined to derive an analytical expression for the lifetime probability density function of the proposed degradation model, providing a theoretical basis for the quantitative reliability assessment of PMSM rolling bearings.
To verify the effectiveness of the proposed method, accelerated degradation tests are conducted on 16 samples under four radial load levels, with synchronous acquisition of vibration and stator current signals. A strong linear correlation is observed between vibration root mean square values and current-based features. Parameter estimation results show that the Hurst index H is greater than 0.5, confirming the presence of LRD in the bearing degradation process. Furthermore, model comparison based on the Akaike information criterion demonstrates that the proposed model outperforms the traditional Wiener and Wiener-Inverse Gaussian models. Cross validation across different stress levels verifies the high extrapolation accuracy of the proposed method, and lifetime predictions under normal operating conditions differ significantly from those of traditional independent-increment models, which highlights the notable impact of LRD on bearing reliability assessment.
Some specific conclusions are summarized as follows: (1) Extracting degradation features from the stator current signal of the motor avoids the need for additional acceleration sensors, which reduces the system deployment cost and invasiveness, and the established linear correlation between current features and vibration features ensures the effectiveness of current features as an indirect evaluation index for bearing degradation states. (2) The proposed accelerated degradation model for PMSM rolling bearings can accurately characterize the LRD of the bearing degradation process by introducing FBM, and combined with the inverse power law acceleration model, it can realize the extrapolation of motor bearing reliability from accelerated stress levels to normal stress levels. (3) Experimental validation results confirm that the proposed degradation model achieves higher accuracy in bearing reliability assessment compared with traditional degradation models based on the Wiener process.
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