A systematic procedure for the analysis of maintenance reports based on a taxonomy and BERT attention mechanism

This work proposes a systematic procedure for analyzing maintenance reports to support maintenance decision-making for a fleet of similar systems. The proposed procedure allows achieving three objectives: (1) grouping maintenance interventions, (2) identifying common characteristics in the maintenance interventions, and (3) recognizing occurrences of rare events of maintenance intervention. Specifically, the attention mechanism of Bidirectional Encoder Representation from Transformer (BERT) and the Density Based Spatial Clustering Applications with Noise (DBSCAN) methods are combined to group maintenance interventions according to their similarity of stated features. A taxonomy of the words used in the textual reports to state the maintenance interventions is developed to systematically identify common features of the clusters, such as the involved components, their working state, the occurred failures or malfunctions, the performed maintenance actions and the personnel that has performed the intervention. The proposed procedure is applied to a repository of reports of maintenance interventions performed on mechanical and electric components of traction systems of a fleet of trains. The obtained results show that it can effectively support decision-making on the maintenance of traction systems.