Fault detection and diagnosis based on sparse representation classification (SRC)

Fault detection and diagnosis (FDD) play an important role in process monitoring applications and remain challenging open problems. Some of existing methods treating fault detection and diagnosis separately are cumbersome and their effects are non-ideal. Some of them may trend to fail when multiple kinds of faults occur owing to the limitation of the typical classification strategy. In this paper, we propose a novel FDD method based on sparse representation classification (SRC), where main contributions are devoted in terms of model training and classification strategy. The motivation behind the SRC is that the reconstruction residuals are very effective to multi-class classification when a faults dictionary is well constructed based on the training samples. Extensive experiments performed on the Tennessee Eastman Process (TEP) demonstrate the effectiveness of the proposed method.