Generating Semantic Matches Between Maintenance Work Orders for Diagnostic Decision Support

Improving maintenance knowledge intelligence using text data is challenging since the maintenance information is mainly recorded as text. To unlock the knowledge from the maintenance text, a decision-making solution based on retrieving similar cases to help solve new maintenance problems is proposed. In this work, an unsupervised domain fine-tuning technique, Transformer-based Sequential Denoising Auto-Encoder (TSDAE) is used to fine-tune the BERT (Bidirectional Encoder Representations from Transformers) model on domain-specific corpora composed of the Maintenance Work Orders (MWOs). Unsupervised fine-tuning helped the BERT model to adapt MWOs text. Results indicate fine-tuned BERT model can generate semantic matches between MWOs regardless of the complex nature of maintenance text.