MIKA_BERT_FMEA_NER

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:

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

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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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