Fault tree analysis improvements: A bibliometric analysis and literature 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
Abstract Fault tree analysis (FTA) is one of the most popular failure analysis techniques that reveal the potential pathways leading to systems or components failure. It has been widely employed in numerous sectors to understand how a system fails and is improved. However, the conventional FTA has been criticized due to a series of inherent shortcomings in the FTA state of the arts. Accordingly, scholars, engineers, and practitioners made their attempts to improve the FTA by dealing with its critical deficiencies over the last decade. However, a few works have been performed to review and synthesize the relevant studies on FTA improvement topics. Thus, the present study is aimed to carry out a systematic literature review of the state‐of‐the‐art theoretical and empirical findings concerning FTA improvement from 2011 to 2021 using the Scopus database collection. In this sense, an in‐depth investigation is conducted using statistical metadata analysis. This subject discusses frontier directions and development trends to reveal the research status. In addition, a bibliometric study is undertaken to ascertain the most productive and influential researchers, research centers, and hotspot fields. It also sheds light on the FTA shortcomings in the existing literature, the evolution in FTA improvement topics, and research opportunities. The outcomes of the present work highlighted that the annual publications on FTA improvement topics are significantly growing, especially after 2019. Besides, Jianxiu Wang, Yan‐Feng Li, and Yihuan Wang are the most productive, prolific, and highly cited authors worldwide; and Asia, particularly China, is the leading contributor in the FTA area. According to Bradford's law, one‐third of all publications (7995) in the field of FTA improvement have been published by 40 sources. Finally, “Decision‐making,” “Risk analysis,” “Uncertainty Analysis,” and “Bayesian Networks” are the four major hot topics integrated into improving the conventional FTA.
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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.044 | 0.222 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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