Learning Reliability Evaluation Models of Power Communication Network Equipments with Capsule Networks

To evaluate reliability of equipments plays a key role for the robustness of modern power communication network. Such tasks imply to learn a regression model with limited samples and heterogeneous formats. In this paper, we address this problem by introducing a deep learning based model containing the latest proposed capsule structures. The model can take raw maintenance data as input directly by treating them as unified document without any extra manual preprocessing. We propose a multiple stages strategy to train the model with original data as well as generated perturbed data from a decoder as augmentation. Experimental results demonstrate that not only the proposed method shows acceptable performance to predict the reliability of communication equipments, but also it shows potentiality especially in learning deep models with fewer samples in different Natural Language Processing tasks. Thus, the proposed capsule networks framework with data generation mechanism could be considered as a promising way to drive deep models in practical learning tasks in which only limited training data is available.

Capsule Networks, Natural Language Processing, Deep Learning, Smart Grid, Reliability Evaluation

Haiyang Wu, Xiao Lu, Dawei Su, Weiwei Miao, Li Zhang, Qi Sun, Learning Reliability Evaluation Models of Power Communication Network Equipments with Capsule Networks. 2019 Scientific Conference on Network, Power Systems and Computing (NPSC 2019), 2019: 1-5. DOI: https://doi.org/10.33969/EECS.V3.001.