The rolling element bearings used in rotating machinery generally include multiple coexisting defects. However, individual defect–induced signals of bearings simultaneously arising from multiple defects are difficult to extract from measured vibration signals because the impulse-like fault signals are very weak, and the vibration signal is commonly affected by the transmission path and various sources of interference. This issue is addressed in this study by proposing a new compound fault feature extraction scheme. Vibration signals are first preprocessed using resonance-based signal sparse decomposition to obtain the low-resonance component of the signal, which contains the information related to the transient fault–induced impulse signals, and reduce the interference of discrete harmonic signal components and noise. The objective used for adaptively selecting the optimal resonance-based signal sparse decomposition parameters adopts the ratio of permutation entropy to the frequency domain kurtosis, as a new comprehensive index, and the optimization is conducted using the cuckoo search algorithm. Subsequently, we apply multipoint sparsity to the low-resonance component to automatically determine the possible number of impulse signals and their periods according to the peak multipoint sparsity values. This enables the targeted extraction and isolation of fault-induced impulse signal features by multipoint optimal minimum entropy deconvolution adjustment. Finally, the envelope spectrum of the filtered signal is used to identify the individual faults. The effectiveness of the proposed scheme is verified by its application to both simulated and experimental compound bearing fault vibration signals with strong interference, and its advantages are confirmed by comparisons of the results with those of an existing state-of-the-art method.

旋转机械中使用的滚动轴承通常包含多个共存的缺陷。然而，由于类似脉冲的故障信号非常弱，并且很难同时从多个振动缺陷中提取出由多个缺陷同时引起的单个轴承缺陷信号，并且振动信号通常会受到传输路径和各种干扰源的影响。本研究通过提出一种新的复合故障特征提取方案来解决此问题。首先使用基于共振的信号稀疏分解对振动信号进行预处理，以获得信号的低共振分量，其中包含与瞬态故障引起的脉冲信号有关的信息，并减少离散谐波信号分量和噪声的干扰。自适应选择基于谐振的最佳信号稀疏分解参数的目标采用置换熵与频域峰度的比值作为新的综合指标，并使用杜鹃搜索算法进行优化。随后，我们将多点稀疏性应用于低谐振分量，以根据峰值多点稀疏性值自动确定可能的脉冲信号数量及其周期。通过多点最佳最小熵反卷积调整，可以有针对性地提取和隔离故障引起的脉冲信号特征。最后，滤波后的信号的包络频谱用于识别各个故障。