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

- 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.

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.