Assessing Observability using Supervised Autoencoders with Application to Tennessee Eastman Process. Issue 2 (2020) - Agarwal, Piyush; Tamer, Melih; Budman, Hector
Abstract: This work presents a novel approach to calculate classification observability using a supervised autoencoder (SAE) neural network (NN) for classification. This metric is based on a minimal distance between every two classes in the latent space defined by the hidden layers of the auto-encoder. Quantification of classification observability is required to address whether the available sensors in a process are sufficient to observe certain outputs (phenomenon) and which additional measurements are to be included in the dataset to improve classification accuracy. The efficacy of the proposed method is illustrated through case-studies for the Tennessee Eastman Benchmark Process.