Probabilistic Fatigue Fragility Curves for Overhead Transmission Line Conductor-Clamp Assemblies
Bibliographic record
Abstract
The residual life of transmission line overhead conductors under conditions of fretting fatigue is an important asset management issue for electric network operators. The current industry practice for overhead conductor residual life estimation relies heavily on experimentally generated fatigue curves or rule-based expert systems. The current experiment-based methods do not consider specific conductor-clamp configurations and are based on the simple flexion model. This approach results in large uncertainties in service life predictions and are limited to a failure criterion based on the first wire failure in the conductor. Rule-based expert systems also have limited applicability since they lack physical representation of the fretting fatigue process. Given the limitations of the current methods, the objective of this work is to propose a framework that combines physics-based models and probability theory to estimate the residual life of overhead conductors considering either single or multiple wire failure criteria. To illustrate this procedure, a finite element model of a Bersfort conductor-clamp system is used to assess the contact conditions and internal stress states in the wires of the conductor. Results from the numerical model are then used to develop a fretting fatigue criterion that is a function of the contact energy dissipation mechanisms, contact stresses, and the plain fatigue resistance of the wires. Probability of failure of each contact point between wires and between wires and clamp is computed using the fretting fatigue criterion. With this information, the most probable locations of fretting fatigue failure are identified in the conductor. The predictions for the locations of failure are validated with available literature data for the same conductor-clamp configuration. Given the probabilities of failures at each contact point, the probability of failure of the conductor is derived with the Poisson binomial distribution. Fragility curves are presented for the first through the third wire failures in the conductor. The fragility curves are validated through comparisons with available literature data on the same conductor-clamp configuration. Fatigue curves are also generated from the fragility model for the first wire failure and compared against experimentally generated fatigue curves.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".