Vibration-based feature extraction of multiple transient fault signals is a challenge in the field of rotating machinery fault diagnosis. Variational mode decomposition (VMD) has great potential for multiple faults decoupling because of its equivalent filtering characteristics. However, the two key hyper-parameters of VMD, i.e., the number of modes and balancing parameter, require to be predefined, thereby resulting in sub-optimal decomposition performance. Although some studies focused on the adaptive parameter determination, the problems in these improved methods like mode redundancy or being sensitive to random impacts still need to be solved. To overcome these drawbacks, an adaptive variational mode decomposition (AVMD) method is developed in this paper. In the proposed method, a novel index called syncretic impact index (SII) is firstly introduced for better evaluation of the complex impulsive fault components of signals. It can exclude the effects of interference terms and concentrate on the fault impacts effectively. The optimal parameters of VMD are selected based on the index SII through the artificial bee colony (ABC) algorithm. The envelope power spectrum, proved to be more capable for fault feature extraction than the envelope spectrum, is applied in this study. Analysis on simulated signals and two experimental applications based on the proposed method demonstrates its effectiveness over other existing methods. The results indicate that the proposed method outperforms in separating impulsive multi-fault signals, thus being an efficient method for multi-fault diagnosis of rotating machines.

多个瞬态故障信号的基于振动的特征提取是旋转机械故障诊断领域的一个挑战。变量模式分解（VMD）由于具有等效的滤波特性，因此具有将多个故障解耦的巨大潜力。但是，VMD的两个关键超参数，即模式数和平衡参数，需要预先定义，从而导致次优的分解性能。尽管一些研究侧重于自适应参数确定，但仍需要解决这些改进方法中的问题，例如模式冗余或对随机影响敏感。为了克服这些缺点，本文提出了一种自适应变分模式分解（AVMD）方法。在提出的方法中，一种新颖的索引称为为了更好地评估信号的复杂脉冲故障分量，首先引入了合相冲击指数（SII）。它可以排除干扰项的影响，并有效地集中于故障影响。基于指标SII选择VMD的最佳参数通过人工蜂群（ABC）算法。这项研究证明了包络功率谱比包络谱具有更强的故障特征提取能力。对模拟信号的分析和基于该方法的两个实验应用证明了其相对于其他现有方法的有效性。结果表明，所提出的方法在分离脉冲多故障信号方面表现优异，是一种有效的旋转机械多故障诊断方法。