metadata

license: other
language:
  - en
pipeline_tag: token-classification
tags:
  - token-classification
  - NER
library_name: transformers
widget:
  - text: >-
      While flying a fire, the UAS experienced an issue of unknown sorts and
      crashed to the ground. From the people watching the aircraft near the
      fire, they seem to think it was some sort of motor failure due to no more
      noise coming from the aircraft and it falling straight to the ground.
    example_title: Example 1
  - text: >-
      During a pre-flight engine run-up, a battery hatch cover disengaged from
      the fuselage and hit one of the vertical takeoff and landing {VTOL}
      propellers. The motor failsafe activated and the motors shut down.
    example_title: Example 2
  - text: >-
      UAS was climbing to 11,000 ft. msl on a reconnaissance mission when it
      experienced a rapid and uncommanded descent. The pilot took no action but
      monitored instruments until the aircraft regained a stable profile. 
    example_title: Example 3

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:

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