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

摘要

Abstract 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. Highlights ? Proposed the Dynamic Sparse Stacked Auto-encoders (DSSAE) Model to extract discriminative features for classification. ? A semi-supervised fault classification methodology is provided based on DSSAE model. ? Case study is done on TE benchmark to show that the DSSAE based fault classification performs better than other methods. ? The increasing of hidden units number in DSSAE model will promote the fault classification accuracy before it reaches steady. ]]>