Robust Fault Detection with missing data via Sparse Decomposition

Data-driven fault diagnosis doesn't depend upon the precise model of an industrial process, however, the quality of the process data set has an important effect on its result. As a quite common phenomenon, the process data sets with missing data usually reduce the performance of data based algorithms. In this paper, a novel data-driven fault detection method based on sparse decomposition is proposed to deal with the issue of missing data. In our approach, the K-SVD and OMP algorithms are used to learn the sparse representation model of the training data and to conduct the sparse decomposition of the testing data, respectively. Compared to the traditional fault detection method, PC A, our sparse decomposition based method outperforms PCA with missing data. At last, simulation experiments verify our result.