A New Deep Model Based on the Stacked Autoencoder with Intensified Iterative Learning Style for Industrial Fault Detection

Abstract Deep learning-based process monitoring methods utilize the features extracted from deep neural networks to perform fault detection and diagnosis. Traditional deep learning models are constructed in a fully connected manner and stacked by a sequential connection style. However, the feature information of the observed data would be discarded as the information is compressed and propagated layer-by-layer in hidden layers. Motivated by this, an intensified iterative learning (IIL) model which is developed from the stacked autoencoder is proposed in this study. In the process of feature extraction of IIL model, the traditional constraints of the hidden layer connection in deep neural networks are disregarded and the feature information of the current hidden layer comes from the information of all previous hidden layers to avoid the loss of information. In the process of real-time process monitoring, the features which are advantageous to accomplish fault detection would be intensified to obtain the most favorable features for monitoring. Finally, Euclidean distance and reconstruction error are employed to indicate and visualize the process status. The monitoring performance of the proposed IIL model is evaluated on three process tasks, and the results show it outperforms other deep learning methods on fault detection.

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