Semi-supervised fault classification based on dynamic Sparse Stacked auto-encoders model

This paper proposes a hierarchical sparse artificial neural network for classifying the faults in dynamic processes base on limited labeled data. The Stacked auto-encoders (SAE) is developed to extract features from different faults. Each neural network in the proposed SAE is given a sparse constraint to learn a Sparse Stacked auto encoders (SSAE). Then, the Dynamic time window is combined into SSAE to build Dynamic Sparse Stacked auto-encoders (DSSAE). DSSAE model based semi-supervised fault classification scheme is then formulated to classify the dynamic faulty data. Simulation studies on the Tennessee-Eastman (TE) benchmark process evaluate the performance of the developed method, which indicate that the DSSAE method performs better than both SAE and SSAE.