Local damage detection in bearings focuses on the identification of periodically impulsive components. Popular methods assume presence of either non-Gaussian noise or different frequency band for informative and non-informative impulses, and use statistics to select appropriate band. Here two impulsive sources occupy the same frequency range: a fault-related signal of interest, and non-cyclic noise describing random events during particular technological process (crushing, sieving etc.). The task is formulated as damage detection in presence of non-Gaussian impulsive noise. We propose to use Nonnegative Matrix Factorization of spectrogram for separation of cyclic and non-cyclic impulsive components. Partial information is fused into a single data set for each component. Finally, post-processing is implemented to allow to recover the time series of each component. New method allows to detect and extract impulsive signal (damage in bearing) in presence high amplitude non-cyclic impulsive signal. Moreover, the algorithm allows to properly indicate lack of any damage.

轴承中的局部损坏检测着眼于周期性脉冲组件的识别。流行的方法假定对于信息性和非信息性脉冲，存在非高斯噪声或不同频带，并使用统计数据选择适当的频带。这里，两个脉冲源占据相同的频率范围：与故障相关的感兴趣信号，以及描述特定工艺过程（粉碎，筛分等）中随机事件的非周期性噪声。该任务被公式化为存在非高斯脉冲噪声时的损坏检测。我们建议使用频谱图的非负矩阵分解来分离循环和非循环脉冲分量。部分信息被融合到每个组件的单个数据集中。最后，实现后处理是为了恢复每个组件的时间序列。新方法允许在存在高振幅非周期性脉冲信号的情况下检测和提取脉冲信号（轴承损坏）。此外，该算法允许正确指示缺少任何损坏。