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.

该研究提出了一种能够盲分离振动源的方法，不需要关于被监测设备或外部测量的任何信息。该方法使用扩张的卷积神经网络初步隔离齿轮信号，然后利用残差的平方对轴承故障信号进行估计。通过一种新颖的基于白化的去卷积方法（WBD）将传递函数的影响从两个源中移除。仿真和实验结果表明，该方法能够在没有额外信息的情况下及早检测到轴承故障。该研究考虑了局部和分布式轴承故障，并假设振动是在稳定的工作条件下记录的。

基于深度学习和去卷积的振动源盲分离