A text mining-based approach for comprehensive understanding of Chinese railway operational equipment failure reports

Railway operational equipment is crucial for ensuring the safe, smooth, and efficient operation of trains. Comprehensive analysis and mining of historical railway operational equipment failure (ROEF) reports are of significant importance for improving railway safety. Currently, significant challenges in comprehensively analyzing ROEF reports arise due to limitations in text mining technologies. To address this concern, this study leverages advanced text mining techniques to thoroughly analyze these reports. Firstly, real historical failure report data provided by a Chinese railway bureau is used as the data source. The data is preprocessed and an ROEF corpus is constructed according to the related standard. Secondly, based on this corpus, text mining techniques are introduced to build an innovative named entity recognition (NER) model. This model combines bidirectional encoder representations from transformers (BERT), bidirectional long short-term memory (BiLSTM) networks, and conditional random fields (CRF), with an additional entity attention layer to deeply extract entity features. This network architecture is used to classify specific entities in the unstructured data of failure reports. Finally, a knowledge graph (KG) is constructed using the Neo4j database to store and visualize the extracted ROEF-related entities and relationships. The results indicate that by constructing the topological relationships of the ROEF network, this study enables the analysis and visualization of potential relationships of historical failure factors, laying a foundation for predicting failures and enhancing railway safety, while also filling the current gap in the mining and analysis of ROEF reports.