A Computational Framework for Effective Representation and Extraction of Knowledge Graph for Power Plant Maintenance and Overhaul

Chenghan Li, Mingchen Li, Hanzhen Lu, Jiabao Pan, Bo Qin, Hongwei Wang

Abstract: The maintenance and overhaul of power plant equipment largely depends on valuable knowledge accumulated during previous projects. This knowledge is often implicit and difficult to capture. This paper aims to address this challenge by proposing a deep learning based framework for the automatic extraction of knowledge from power plant maintenance reports in the form of knowledge graph (KG). Unlike other work, this framework can support effective text classification and Named Entity Recognition (NER) tasks for fire power plants reports. Specifically, we develop a TextCNN-GRU method with multi-attention to classify power plant maintenance text effectively, and use the BERT-BiLSTM-CRF model for NER tasks. To evaluate the proposed framework, we conduct experiments on a dataset collected from a realworld power plant, and the results obtained show outstanding performance. This work hence opens up opportunity for supporting intelligent maintenance of lire power plants using deep learning based methods and KG.