Manager for Intelligent Knowledge Access (MIKA)
Custom Named-Entity Recognition (NER) for Failure Modes and Effects Analysis (FMEA)
base-bert-uncased model first further pre-trained then fine-tuned for custom NER to extract failure-relevant entities from incident and accident reports. The model was trained on manually annotated NASA LLIS reports and evaluated on SAFECOM reports.
NER model training was for 4 epochs with:BertForTokenClassification.from_pretrained , learning_rate=2e-5, weight_decay=0.01,
The model was trained to identify the following long-tailed entities:
- CAU: failure cause
- MOD: failure mode
- EFF: failure effect
- CON: control process
- REC: recommendations
Performace:
| Entity | Precision | Recall | F-1 | Support |
|---|---|---|---|---|
| CAU | 0.31 | 0.19 | 0.23 | 1634 |
| CON | 0.49 | 0.34 | 0.40 | 3859 |
| EFF | 0.45 | 0.20 | 0.28 | 1959 |
| MOD | 0.19 | 0.52 | 0.28 | 594 |
| REC | 0.30 | 0.59 | 0.40 | 954 |
| Average | 0.41 | 0.32 | 0.33 | 9000 |
More infomation on training data, evaluation, and intended use can be found in the original publication
Citation: S. R. Andrade and H. S. Walsh, "What Went Wrong: A Survey of Wildfire UAS Mishaps through Named Entity Recognition," 2022 IEEE/AIAA 41st Digital Avionics Systems Conference (DASC), Portsmouth, VA, USA, 2022, pp. 1-10, doi: 10.1109/DASC55683.2022.9925798. https://ieeexplore.ieee.org/abstract/document/9925798
Notices:
Copyright © 2023 United States Government as represented by the Administrator of the National Aeronautics and Space Administration. All Rights Reserved.
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