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. [ABSTRACT FROM AUTHOR]