Two-stage attention network for fault diagnosis and retrieval of fault logs

In industrial systems, textual failure records note the failure mechanisms, the parts involved, and the failure symptoms; these records guide fault analysis and repair. However, case retrieval and feature extraction require extensive prior knowledge and diagnostic expertise; this method is time-consuming and labor-intensive. In this article, we present a novel two-stage framework that automatically extracts and generates features from very large textual records. We use an improved weighted latent Dirichlet allocation model and the Word2vec method to extract topic category and semantic features from fault texts; this approach accelerates training convergence. Next, we build a topic-context attention model in which word-embedding semantic features interact with topic features. Finally, we use classification and similarity calculation models to diagnose faults and retrieve similar cases; this approach ensures feature generation. Our method is very granular in terms of case representation which significantly improves diagnosis. The method robustly identifies similar cases by interrogating vehicle maintenance records.