Ensemble Linear Regression and Paris’ Law Based Methods for Structure Fatigue Crack Length Estimation and Prediction Using Ultrasonic Wave Data
Bibliographic record
Abstract
This paper presents the methods developed for the 2019 PHM Conference Data Challenge. The PHM Data Challenge aims to estimate the fatigue crack length of a type of aluminum structure using ultrasonic signals at the current loading cycle, and to predict the crack length at multiple future loading cycles (multiple-step ahead prediction) as accurate as possible. For the crack length estimation, four crack sensitive features are extracted from ultrasonic signals, namely, the first peak value, root mean squared value, log of kurtosis and correlation coefficient. These features and their 2nd order interactions are used as inputs for an ensemble linear regression model which is built to map the features to crack length. The Best Subset Selection method is employed to select optimal features. For the crack length prediction, variations of the Paris’ law are derived to describe the mutual relationships between crack length and the number of loading cycles. The specimen material parameters and stress range of the Paris’ law are learned using the Genetic Algorithm, and updated with the next-step predicted crack length. The crack length corresponding to a future loading cycle number under constant amplitude cyclic loading and under variable amplitude cyclic loading is predicted respectively. The presented overall methodology achieves a score of 16.14 with the score calculation rule provided by the Data Challenge organization team, and ranks 3rd among all teams.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".