Transient impulses caused by local defects are critical for the fault detection of rotating machines. However, they are extremely weak and overwhelmed in the strong noise and harmonic components, making the transient features are very difficult to be extracted. This paper proposes an adaptive multi-scale improved differential filter (AMIDIF) to enhance the identification of transient impulses for rotating machine fault diagnosis. In this scheme, firstly, the AMIDIF is performed to decompose the measured signal of rotating machine into a series of multi-scale improved differential filter (MIDIF) filtered signals. Subsequently, in view of the MIDIF filtered signals exhibit varying extents of validity in revealing fault features, a weighted reconstruction method using correlation analysis is proposed in which the weighted coefficients are counted and distributed to the corresponding MIDIF filtered signals to highlight the effective MIDIF filtered signals and weaken the invalid ones. Finally, the transient impulse components of rotating machinery are obtained by multiplying the weighted coefficients and the MIDIF filtered signals under different scales. Furthermore, the fault types of rotating machines are inferred from the fault defect frequencies in the envelope spectrum of the transient impulses. Simulation analysis and experimental studies are implemented to verify the performance of the AMIDIF compared with the state-of-the-art methods including spectral kurtosis (SK), multi-scale average combination different morphological filter (ACDIF) and multi-scale morphology gradient product operation (MGPO). The results prove that the AMIDIF has excellent performance in extracting transient features for rotating machines fault diagnosis.

由局部缺陷引起的瞬态脉冲对于旋转电机的故障检测至关重要。然而，它们极其微弱，在强噪声和谐波分量中不堪重负，使得瞬态特征很难被提取。本文提出了一种自适应多尺度改进微分滤波器 (AMIDIF)，以增强对瞬态脉冲的识别，用于旋转电机故障诊断。在该方案中，首先进行AMIDIF将旋转电机的测量信号分解为一系列多尺度改进差分滤波器（MIDIF）滤波信号。随后，鉴于 MIDIF 滤波信号在揭示故障特征方面表现出不同程度的有效性，提出了一种基于相关分析的加权重建方法，将加权系数统计并分配给相应的MIDIF滤波信号，以突出有效的MIDIF滤波信号，弱化无效的信号。最后，将加权系数与不同尺度下的MIDIF滤波信号相乘，得到旋转机械的瞬态脉冲分量。此外，从瞬态脉冲包络谱中的故障缺陷频率推断旋转电机的故障类型。实施模拟分析和实验研究，以验证 AMIDIF 与包括谱峰度 (SK) 在内的最先进方法相比的性能，多尺度平均组合不同形态滤波器（ACDIF）和多尺度形态梯度乘积运算（MGPO）。结果证明AMIDIF在提取瞬态特征用于旋转电机故障诊断方面具有优异的性能。