Machine Learning Applications for Reliability Engineering: A Review
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The treatment of big data as well as the rapid improvement in the speed of data processing are facilitated by the parallelization of computations, cloud computing as well as the increasing number of artificial intelligence techniques. These developments lead to the multiplication of applications and modeling techniques. Reliability engineering includes several research areas such as reliability, availability, maintainability, and safety (RAMS); prognostics and health management (PHM); and asset management (AM), aiming at the realization of the life cycle value. The expansion of artificial intelligence (AI) modeling techniques combined with the various research topics increases the difficulty of practitioners in identifying the appropriate methodologies and techniques applicable. The objective of this publication is to provide an overview of the different machine learning (ML) techniques from the perspective of traditional modeling techniques. Furthermore, it presents a methodology for data science application and how machine learning can be applied in each step. Then, it will demonstrate how ML techniques can be complementary to traditional approaches, and cases from the literature will be presented.
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.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it