关键词: 故障监测, 非负矩阵分解, 主元分析, 稀疏编码, 统计过程监控

Abstract: In this paper, a novel fault detection method based on sparse non-negative matrix factorization (SNMF) is proposed. NMF (non-negative matrix factorization) is a new dimension reduction technique that can find a low-rank matrix approximation from the original data. In contrast to the conventional multivariate statistical process monitoring methods, for example PCA, NMF has no assumption about the nature of latent variables, except for non-negativity. Combining linear sparse coding and NMF, SNMF can learn much sparser representation via imposing sparseness constraints. During factorization, low-rank matrix is orthogonalized to remove redundant information and concentrate information on fewer directions of projection. Then, SNMF is used to extract the latent variables that drive a process and new statistical metrics are defined for fault detection. Kernel density estimation (KDE) is adopted to calculate the confidence limits of defined statistical metrics. Afterwards, the proposed method is applied to the Tennessee Eastman process to evaluate the monitoring performance, comparing with conventional NMF and PCA. The results from the experiment show the feasibility of the new method.

Key words: fault detection, non-negative matrix factorization, PCA, sparse coding, statistical process monitoring