Λεπτομέρειες βιβλιογραφικής εγγραφής
| Τίτλος: |
基于改进 SSA-VMD 和自相关滤波的滚动轴承 故障诊断方法. (Chinese) |
| Alternate Title: |
Fault diagnosis method based on improved SSA-VMD and autocorrelation filtering for rolling bearing. (English) |
| Συγγραφείς: |
桑硕海, 王浩伟, 赵建忠, 韩丹阳 |
| Πηγή: |
Systems Engineering & Electronics; Sep2026, Vol. 48 Issue 9, p3066-3078, 13p |
| Θεματικοί όροι: |
Fault diagnosis, Roller bearings, Noise control, Spectrum analysis instruments, Signal processing, Optimization algorithms |
| Abstract (English): |
Rolling bearing fault diagnosis is susceptible to noise interference, while variational mode decomposition (VMD) parameter optimization is prone to local optima and intrinsic mode function (IMF) selection relies heavily on empirical thresholds, thereby limiting the accurate extraction of fault features. To address these issues, an improved sparrow search algorithm (SSA) -VMD and autocorrelation adaptive-filtering method is proposed. A composite fitness function integrating minimum envelope entropy, kurtosis, and the crest factor of the envelope spectrum is constructed, and the Sparrow Search Algorithm is improved through population initialization as well as discoverer and follower updating strategies to adaptively optimize the VMD mode number and penalty factor. Subsequently, normalized autocorrelation weights are used to adaptively filter and reconstruct the IMFs, followed by envelope-spectrum analysis, thereby avoiding manual IMF-selection thresholds. Experiments on three public bearing-fault datasets show that the improved SSA increases convergence speed by 80%, 20%, and 57%, respectively, while achieving lower fitness values, and the proposed method obtains an average fault diagnosis accuracy above 99.074%. These results demonstrate that the method reduces reliance on expert experience in VMD parameter optimization and IMF selection, while improving fault-feature extraction and diagnostic accuracy for rolling bearings under noisy conditions. [ABSTRACT FROM AUTHOR] |
| Abstract (Chinese): |
针对滚动轴承故障诊断易受噪声干扰, 同时变分模态分解 (variationalmode decomposition, VMD) 参数 寻优易陷入局部最优且固有模态函数 (intrinsic mode function, IMF) 筛选依赖经验阈值, 制约了故障特征的准确提 取。为此, 提出一种改进麻雀搜索算法 (sparrowsearch algorithm, SSA) -VMD 与自相关自适应滤波融合的滚动轴承 故障诊断方法构建融合最小包络熵、峭度和包络谱峰值的适应度函数, 并从种群初始化、发现者更新和追随者更 新三个环节改进 SSA, 实现 VMD 模态数和惩罚因子的自适应寻优。随后, 利用归一化自相关权重对各 IMF 进行 自适应滤波、重构及包络谱分析, 避免人工设置 IMF 筛选阈值。在 3 组公开轴承故障数据集上的实验表明, 改进 SSA 相较于对应对比算法的收敛速度分别提高 80% 、20% 和 57%, 同时获得更低的适应度值, 所提方法的平均故 障诊断准确率达到 99.074% 以上。结果表明, 该方法能够降低 VMD 参数寻优和 IMF 筛选对人工经验的依赖, 提 高噪声条件下滚动轴承故障特征提取与诊断的准确性。 [ABSTRACT FROM AUTHOR] |
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| Βάση Δεδομένων: |
Complementary Index |