A Survey on Arc Fault Detection and Wire Fault Location for Aircraft Wiring Systems
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
<div class="htmlview paragraph">This paper provides an overview of recent developments in the area of arc fault detection (AFD) and wire fault location for aircraft wiring systems. Arc faults have been identified as one of the greatest threats to human lives and properties, and the likely cause of several aircraft disasters. With the introduction of high voltage transmission in aircraft to reduce the wiring weight and to meet the increasing power demands, the probability of initiating and sustaining continuous arcs in modern aircraft have been increased. However, arc faults are hard to detect and wiring problems are difficult to locate in aircraft, due to their complex profiles, high impedance property, and pressure sensitive characteristic, etc. The difficulty in resolving this problem is also due to the fact that false alarms cannot be tolerated but missing alarms can be fatal, and arc faults are normally intermittent as a result of the in-flight vibration.</div> <div class="htmlview paragraph">For arc fault detection, a great number of methods have been proposed, which can be categorized into mechanical and electrical methods; in general, features can be extracted from time domain, frequency domain, or time-frequency domain; and algorithms have been developed based on adaptive techniques, Kalman filter, fractal theory, neural networks, fuzzy logic reasoning, and expert systems to enhance arc fault detection. Regarding wire fault location, numerous techniques have been developed and practiced, including visual inspection, impedance measurement, pulse arrested spark discharge diagnostics, high voltage test, inert gas method, and various kinds of reflectometry. Each of these techniques is effective to a certain extent, but all of them come with drawbacks as well.</div> <div class="htmlview paragraph">This paper is intended to provide a constructive summary of the present arc fault detection and wire fault location techniques, together with discussions on the current and future research and development opportunities and challenges in this field.</div>
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it