The incipient bearing fault diagnosis is crucial to the industrial machinery maintenance. Developed based on the blind source separation, blind source extraction (BSE) has recently become the focus of intensive research work. However, owing to certain industrial restrictions, the number of sensors is usually less than that of the source signals, which is defined as an underdetermined BSE problem to identify the fault signals. The kernelized methods are found to be robust to the noise, especially in the presence of outliers, which makes it a suitable tool to extract fault signatures submerged in the strong environment noise. Thus, this paper proposes a new underdetermined BSE method based on the empirical mean decomposition and kernelized correlation. The experimental results indicate that the extracted fault signature presents more obvious periodicity. Two important parameters of this method, including the multi-shift number and the kernel size are investigated to improve the algorithm performance. Furthermore, performance comparisons with underdetermined BSE based on the second order correlation are made to emphasize the advantage of the presented method. The application of the proposed method is validated using the simulated signal and the rolling element bearing signal of the train vehicle axle.

早期轴承故障诊断对于工业机械维护至关重要。基于盲源分离开发的盲源提取（BSE）最近已成为深入研究工作的重点。但是，由于某些工业限制，传感器的数量通常少于源信号的数量，这被定义为无法确定故障信号的BSE问题。发现采用核化的方法对噪声具有鲁棒性，特别是在存在异常值的情况下，这使其成为提取淹没在强环境噪声中的故障特征的合适工具。因此，本文提出了一种基于经验均值分解和核相关的欠定BSE方法。实验结果表明，提取的故障特征具有明显的周期性。研究了该方法的两个重要参数，包括多移位数和内核大小，以提高算法性能。此外，基于二阶相关性与未确定的BSE进行性能比较，以强调所提出方法的优势。通过仿真信号和火车车轴滚动轴承信号验证了该方法的应用。基于二阶相关性与欠定BSE进行性能比较，以强调本方法的优势。通过仿真信号和火车车轴滚动轴承信号验证了该方法的应用。基于二阶相关性与欠定BSE进行性能比较，以强调本方法的优势。通过仿真信号和火车车轴滚动轴承信号验证了该方法的应用。