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Parametric adaptive VMD method and its applications on early fault diagnosis for rolling bearing

机译：参数自适应VMD方法及其在滚动轴承早期故障诊断中的应用

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This paper proposes an improved parametric adaptive variational mode decomposition (VMD) method, in view of the problem with the traditional VMD method in defining parameters the mode number K and the penalty parameter α. First, taking fault impacts and periodicity into consideration, this paper adopts the Gini index and spectrum peak ratio (SPR) index as the basis to construct a weighted spectrum peak ratio (WSPR) index. After that, it takes the maximization of the WSPR index as the optimization objective, using the whale optimization algorithm (WOA) to find the optimal parameter combination for K and α, and avoids the disadvantages of subjective parameters selection. Finally, it follows the maximum WSPR principle to automatically select a sensitive intrinsic mode function (IMF) and extract fault characteristics via envelope demodulation analysis. The proposed method is verified through experimental signal. As shown by the results, this method can effectively extract weak characteristics of early fault signals and offer the accurate judgment of fault types.
机译：针对传统变分模式分解（VMD）方法在定义模式数K和惩罚参数α时存在的问题，提出了一种改进的参数自适应变分模式分解（VMD）方法。首先，考虑到断层的影响和周期性，本文以基尼指数和谱峰比（SPR）指数为基础，构造了加权谱峰比（WSPR）指数。然后，以WSPR指标最大化为优化目标，采用whale优化算法（WOA）寻找K和α的最佳参数组合，避免了主观参数选择的缺点。最后，它遵循最大WSPR原则，自动选择敏感的固有模式函数（IMF），并通过包络解调分析提取故障特征。通过实验信号验证了该方法的有效性。结果表明，该方法能有效地提取早期故障信号的微弱特征，并能准确判断故障类型。

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