Vibrations of rotating machinery primarily originate from two sources, both of which are distorted by the machine's transfer function on their way to the sensor: the dominant gear-related vibrations and a low-energy signal linked to bearing faults. The proposed method facilitates the blind separation of vibration sources, eliminating the need for any information about the monitored equipment or external measurements. This method estimates both sources in two stages: initially, the gear signal is isolated using a dilated CNN, followed by the estimation of the bearing fault signal using the squared log envelope of the residual. The effect of the transfer function is removed from both sources using a novel whitening-based deconvolution method (WBD). Both simulation and experimental results demonstrate the method's ability to detect bearing failures early when no additional information is available. This study considers both local and distributed bearing faults, assuming that the vibrations are recorded under stable operating conditions.

翻译：旋转机械的振动主要源自两个部分，两者在传递至传感器的过程中均受到机器传递函数的畸变影响：主导的齿轮相关振动以及与轴承故障相关的低能量信号。所提方法实现了振动源的盲分离，无需任何监测设备信息或外部测量。该方法分两阶段估计两种源信号：首先，利用扩张卷积神经网络提取齿轮信号；随后，通过残差信号的平方对数包络估计轴承故障信号。采用一种新型白化基反卷积方法（WBD）消除两源信号中的传递函数效应。仿真与实验结果表明，在无附加信息的情况下，该方法能够实现轴承故障的早期检测。本研究同时考虑了局部与分布轴承故障，假设振动信号在稳态工况下采集。