Layered and Real-Valued Negative Selection Algorithm for Fault Detection
Bibliographic record
Abstract
In this paper, the challenging task of the development of a generic fault detection (FD) method is addressed. While the past FD research has primarily focused on modeling and signal-processing methods that are problem specific and require complete knowledge of the system model and fault types, this paper presents a novel layered and real-valued negative-selection algorithm (LRNSA)-based FD method independent of prior knowledge of fault types and patterns. Specifically, in the training phase, the nonself-space is divided into different layers for effective generation and distribution of detectors using normal (self) data. The major accomplishments of the proposed method are improved nonself-space coverage of the uncovered gaps (holes), followed by the formation of cluster detector with large radius. To test the capabilities of the developed method, the generated specialized detector distribution is studied using bearing fault modeling in a three-phase induction motor. The proposed method is subsequently investigated and validated by applying it to an actual induction motor for different types of bearing faults. Finally, the comparative results on the benchmark dataset demonstrate the superiority of the proposed method compared to the state-of-the-art machine learning algorithms in terms of higher FD accuracy and quick detection with reduced online detection time.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".