OODCN: Out-Of-Distribution Detection in Capsule Networks for Fault Identification

Abstract

In order to aid survey engineers identify Partial Discharge (PD) types during their asset diagnostics, we develop an image-based system for PD signal classification and Out-Of-Distribution (OOD) rejection. First, the PD signal is converted to a Phase-Resolved PD (PRPD) image. Then, the image is passed to the system which exploits a Capsule Network in an auto-encoder framework, where the encoder output is used for PD classification and the decoder output is used in the OOD decision. The latter is the main contribution of this work which combines the decoder part with a reconstruction metric evaluating the difference between the original and reconstructed image. A threshold for OOD decision is introduced based on the distribution of reconstruction values from the training data. Most importantly, the OOD data is not exposed to the model during training. Results demonstrate high performance in both PD types classification and OOD detection tasks using synthetic and real data.