This paper presents an experimental dataset developed for the detection of parallel arc faults in aircraft electrical systems. This dataset is based on a total of 960 experiments performed in a low-pressure chamber under different conditions using two electrodes placed on the surface of an insulating material. These experiments correspond to 2 insulating materials, 12 electrode distances, and 10 pressure conditions representative of aircraft environments. Each experimental condition was repeated four times, resulting in 960 experimental recordings, each containing one million samples of time, current, and voltage signals of the electric arc induced on the surface of the insulating material. The dataset can be used to model arc behavior under different pressure conditions, to identify patterns that indicate the presence of an arc, and to accelerate the improvement of arc identification. This dataset has the potential to be used to develop arc fault detection and identification methods for more electric and all-electric aircraft and other electric vehicles.
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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11659625 | PMC |
| http://dx.doi.org/10.1038/s41597-024-04253-5 | DOI Listing |
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