Sparse subspace clustering (SSC) is an effective method to cluster sensing signal for fault diagnosis in mechanical systems. SSC is based on a global expression strategy describing each data point by other data points from all the potential clusters. A drawback of this strategy is that it generates nonzero elements in the nondiagonal blocks of the similarity matrix, thereby reducing the performance of the matrix for data discrimination. To improve SSC’s performance, a composite-graph-based SSC (CG-SSC) method is developed by introducing distance among the data points into SSC, where the L1-norm of the sparse coefficients is replaced by the product of the data distance and the sparse coefficients. The distance serves as a constraint to the amplitude of the sparse coefficients. A sparse coefficient with small amplitude is assigned if the distance between the expressed data and the expressing data is large, and vice versa. This method supports the construction of a compact cluster. The effectiveness of the presented method is experimentally verified by data measured on several bearings and gearboxes with different types of faults. The method is also compared with classical clustering methods, and the results indicate its advantage for data sorting and clustering.

中文翻译：

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用于机器故障诊断的基于复合图的稀疏子空间聚类

稀疏子空间聚类（SSC）是一种对传感信号进行聚类以进行机械系统故障诊断的有效方法。SSC 基于全局表达策略，通过来自所有潜在集群的其他数据点来描述每个数据点。这种策略的一个缺点是它在相似矩阵的非对角块中生成非零元素，从而降低了矩阵对数据区分的性能。为了提高 SSC 的性能，通过将数据点之间的距离引入 SSC，开发了一种基于复合图的 SSC（CG-SSC）方法，其中稀疏系数的 L1 范数被数据距离和稀疏系数。距离用作对稀疏系数幅度的约束。如果表达数据与表达数据之间的距离大，则分配幅度小的稀疏系数，反之亦然。该方法支持构建紧凑集群。通过在具有不同类型故障的几个轴承和齿轮箱上测量的数据，通过实验验证了所提出方法的有效性。该方法还与经典聚类方法进行了比较，结果表明其在数据排序和聚类方面的优势。