Characteristic parameter change of circuit breaker under closing spring fatigue
Bibliographic record
Abstract
Closing spring fatigue faults of high voltage circuit breakers affect the timing parameters in closing operations and reduce the closing performance of the circuit breaker. Traditional tests of timing parameter based on travel curve cannot be applied online, and sensor installation is complicated. In this paper, a new method to extract key circuit breaker timing parameters from the vibration signal under closing spring fatigue fault is proposed. First, the travel curve of the circuit breaker under closing spring fatigue is simulated in Automatic Dynamic Analysis of Mechanical Systems (ADAMS). Results indicate that the time intervals between key points of the travel curve can be used as fault features. Then, according to the working principle of the circuit breaker’s spring operating mechanism, the vibration event caused by component impact in the closing operation is analyzed. The corresponding timing parameters are extracted from the vibration signal using the double threshold method based on the short-time energy to entropy ratio. Finally, comparison of experimental measurements with ADAMS simulation results and vibration extraction provides the change law of the fault feature. The correctness of the proposed method is verified. This paper presents a new method for online monitoring of circuit breaker closing spring fatigue.
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.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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 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".