Actuator fault detection and isolation system for multirotor unmanned aerial vehicles
Bibliographic record
Abstract
This article presents a new actuator fault detection and isolation method for multi rotor unmanned aerials (UAVs). The UAV community raises the need to develop a highly efficient classifier capable of early detection of failures and fully inde-pendent of other systems. This paper presents the entire process of preparing the method discussed and its implementation in the embedded system. The measurements of four accelerometers were digitally processed data, the main element of which was the frequency domain analysis. The feature vectors prepared in this way were used to train the artificial neural network. The network model has been implemented on a microcontroller. The tests were carried out using data collected during actual flight in various configurations of damaged propellers. The overall accuracy of the proposed method was 98.08 % without the presence of false alarms. The total processing time was also tested, demonstrating real-time classification capability onboard the autonomous flying robot. The efficiency results were compared with the random forest method.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".