Academic Journal
参数自适应变分模态分解的滚动轴承故障诊断.
| Τίτλος: | 参数自适应变分模态分解的滚动轴承故障诊断. (Chinese) |
|---|---|
| Alternate Title: | Rolling Bearing Fault Diagnosis Based on Parameters Adaptive VMD. (English) |
| Συγγραφείς: | 李永琪 |
| Πηγή: | Light Industry Machinery; Aug2026, Vol. 44 Issue 4, p61-67, 7p |
| Θεματικοί όροι: | Fault diagnosis, Metaheuristic algorithms, Entropy (Information theory), Spectrum analysis instruments |
| Abstract (English): | To effectively identify rolling bearing faults and address the adaptive optimization of Variational Mode Decomposition (VMD) parameters, the author proposed using Global Search Whale Optimization Algorithm (GS-WOA) to optimize VMD parameters, combined with Weighted Ensemble Kurtosis (WEK) to select the optimal Intrinsic Mode Function (IMF), thereby achieving effective diagnosis of rolling bearing faults. Firstly the amplitude of each mode was analyzed using the Hilbert marginal spectrum to achieve the optimal adjustment of the VMD parameter range. Secondly, the minimum fuzzy entropy was adopted as the fitness function of GS-WOA for VMD decomposition the optimal intrinsic mode component was selected by maximizing WEK, and envelope spectrum analysis was performed on this component to accurately identify the type of rolling bearing fault. Finally, the bearing simulation signal and the actual measured inner ring fault data were collected for experimental verification to validate the effectiveness and practicality of the proposed method. The results show that compared with the sequential variational mode decomposition and the empirical mode decomposition method the envelope spectrum obtained by this method is clearer and the fault frequency is more obvious. The fault frequency of the inner ring was 162. 19 Hz. This method provides an adaptive analysis approach for bearing fault diagnosis that does not require manual preset parameters, has high accuracy and strong anti-noise ability. [ABSTRACT FROM AUTHOR] |
| Abstract (Chinese): | 为有效识别滚动轴承故障, 针对变分模态分解(Variational Mode Decomposition, VMD)参数的自适应优化, 笼者提 出了基于全局搜寻策略的鲸鱼优化算法(Global Search-Whale Optimization Algorithm, GS-WOA)优化VMD参数, 结合加权 合成峭度(Weighted Ensemble Kurtosis, WEK)选取最优固有模态函数(Intrinsic Mode Function, IMF),实现了滚动轴承故 障有效诊断。首先, 利用Hilbert边际谱对各个模态的振幅进行分析, 实现VMD参数范围的最优调节;其次, 采用最小模 糊熵为GS-WOA算法的适应度函数进行VMD分解, 通过加权合成峭度最大化选取最犹固有模态分量, 并对该分量进行 包络谱分析, 进而实现滚动轴承故障类型的精准判别;最后, 采集轴承仿真信号与实测内圈故障数据进行试验验证。结 果表明: 与逐次变分模态分解算法、经验模态分解方法相比, 笔者提出的方法获得的包络谱更清晰, 且故障频率更明显, 内圈故障频率为162. 19 Hz。笔者提出的方法为轴承故障诊断提供了一种无需人工预设参数, 且精度高、抗噪性强的自 适应分析思路。. [ABSTRACT FROM AUTHOR] |
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| Βάση Δεδομένων: | Complementary Index |
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