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.