Deep fisher autoencoder combined with self-organizing map for visual industrial process monitoring

Feature extraction plays an important role in industrial process monitoring. Autoencoder and its deep framework, deep autoencoder, are used to extract latent features from complex data. However, the latent features extracted by autoencoder through unsupervised learning may not be useful for discriminative tasks. Fisher discriminant analysis (FDA) is another widely used supervised feature extraction technique that take full advantage of the Fisher criterion to enable the extracted discriminative features to maximize inter-class distance while minimizing intra-class distance. Drawing on FDA and autoencoder, this study proposes Fisher autoencoder (FAE) to extract discriminative features. FAE uses the Fisher criterion to guide the autoencoder in minimizing the reconstruction error while enabling the extracted features by the hidden layer to increase the separation between classes. We stack FAE to derive deep FAE (DFAE) for feature extraction, then we combine DFAE with self-organizing map (DFAE-SOM), which is a tool typically used in visualization for visual process monitoring. Tennessee Eastman process and an actual dataset of the blade icing of wind turbine are applied to test the performance of DFAE-SOM. The experiment demonstrates that DFAE increases the separation between classes more than DAE and other standard techniques. Therefore, DFAE is conducive to visualization and improves the accuracy of process monitoring.