基于聚类优化的非负矩阵分解方法及其应用

针对不断增加的机电系统运行状态信息，传统的特征提取和选择方法已无法满足需求。根据非负矩阵分解典型算法的特点，基于非负矩阵分解的聚类特性，提出了一种面向故障诊断的分解方法。通过分类能力和迭代效率的对比分析，选择了相关性约束和稀疏性约束的改进型交替最小二乘迭代算法，确定了低维嵌入维数及迭代初始化方法，在UCI测试数据集和TEP系统的特征选择应用中验证了该方法的有效性。

Non-negative Matrix Factorization Based on Clustering and Its Application

With the increasing complexity of electromechanical system state informations, traditional feature extraction and selection methods were unable to meet the needs. According to the characteristics of conventional non-negative matrix factorization(NMF) algorithm, a NMF method for monitoring and fault diagnosis was proposed based on the clustering property of NMF. By comparing classification accuracy and iteration efficiency, an improved alternating least square iterative algorithm with sparsity and correlation constraints was selected, and the low-dimensional embedded dimension and iterative initialization method were also determined. Experimental results to UCI test datasets and fault diagnosis of Tennessee-Eastman process(TEP) systems show that this approach is more effective to extract the fault features, and enhance the failure pattern capabilities.