Multichannel signals collected by multiple sensors contain richer condition information of equipment than single-channel signals. However, such issues as simultaneous denoising, adaptive decomposition and synchronous extraction are still challenging for multichannel signals, which are beneficial to accurate fault diagnosis. Thus, a useful multichannel feature synchronous extraction tool is proposed for detecting rolling bearing faults, named as Msegram. First, a tensor synchronization denoising method based on high order singular value decomposition (HOSVD) is proposed for multichannel signal preprocessing. Original multichannel signals of testing bearings are constructed to be a third-order tensor by phase space reconstruction. Hereinto, a singular entropy increment is adopted to determine a reasonable singular order for each unfolding, and an optimal core tensor is obtained for local reconstruction analysis. Second, multi-layer K-value multivariate variational mode decomposition (MVMD) is designed after the multichannel noise reduction to realize synchronous adaptive filtering and decomposition for the multichannel signals. Third, inspired by the idea of the spectral kurtosis, a tower-shaped crest factor of envelope spectrum (EC) diagram similar to Fast Kurtogram (FK) is proposed to visualize the output of multichannel bearing fault feature results. According to the tower-shaped EC diagram with the maximum fault crest factor, the optimal analytic results of multichannel signals are selected and output to synchronously extract bearing fault features. Finally, repeatable simulations and two experimental fault cases of rolling bearings are implemented to demonstrate the practicability and effectiveness of the proposed method. The results show that the proposed method can successfully reveal the compound faults from experimental bearing and effectively identify the compound faults from locomotive wheelset bearing.

多个传感器采集的多通道信号比单通道信号包含更丰富的设备状态信息。然而，同时去噪、自适应分解和同步提取等问题对于多通道信号仍然具有挑战性，这有利于准确的故障诊断。因此，提出了一种有用的多通道特征同步提取工具，用于检测滚动轴承故障，命名为 Msegram。首先，针对多通道信号预处理提出了一种基于高阶奇异值分解（HOSVD）的张量同步去噪方法。通过相空间重构将测试轴承的原始多通道信号构造为三阶张量。其中，采用奇异熵增量来确定每次展开的合理奇异顺序，并获得最佳核心张量用于局部重建分析。二、多层钾在多通道降噪之后设计了-值多元变分模态分解(MVMD)，实现对多通道信号的同步自适应滤波和分解。第三，受谱峰度思想的启发，提出了一种类似于快速峰度图（FK）的塔形波峰因数包络谱（EC）图，用于可视化多通道轴承故障特征结果的输出。根据具有最大故障波峰因数的塔形EC图，选择并输出多路信号的最优分析结果，同步提取轴承故障特征。最后，通过滚动轴承的可重复仿真和两个实验故障案例来证明所提出方法的实用性和有效性。