Quality-relevant fault detection based on adversarial learning and distinguished contribution of latent variables to quality

Quality-relevant fault detection is a primary task to reveal the changes of quality variables in process monitoring. Current works mainly focus on learning quality-relevant features, however, how to distinguish quality-relevant and irrelevant information is responsible for the excellent monitoring performance. In this study, a novel quality relevant fault detection method is proposed on the basis of adversarial learning and distinguished contribution of latent features to quality is originally introduced. First of all, we map the input variables into a gaussian manifold in adversarial and unsupervised manner. Then a fully connected neural network is trained to learn the relationship between latent and quality variables. To distinguish necessary information in such manifold, the Jacobi operator at the corresponding point is calculated to project the latent variables into quality-relevant and quality irrelevant subspaces. Third, fault detection is implemented in these dynamic subspaces using the probabilities of latent variables. Finally, the proposed method is evaluated by numerical example, the Tennessee-Eastman process and wind turbine blade icing process.

Automated summary

This study proposes a novel quality-relevant fault detection method based on adversarial learning, emphasizing the concept of distinguishing contributions. The input variables are first mapped into a Gaussian manifold, followed by training a neural network to learn the relationship between latent and quality variables. Fault detection is then implemented in dynamic subspaces using the probabilities of latent variables, with evaluation on numerical examples, the Tennessee-Eastman process, and wind turbine blade icing process.