Dynamic Failure Analysis of Ship Energy Systems Using an Adaptive Machine Learning Formalism
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 criticality of shipping operations in global trade requires a comprehensive understanding of its sustainability. This depends onthe integrity/performance of the ship structure and vital systems, such as the ship propulsion engine. The current research paper presents the application of an adaptive machine learning formalism, the Bayesian network, for failure assessment of a ship propulsion engine considering nonlinear and nonsequential failure interactions. The model captures critical failure influencing factors and their complex interactions to predict the failure probability of the ship energy system. Sensitivity and uncertainty analysis was carried out to establish the degree of influence of vital failure influencing factors as they affect the ship propulsion engine’s reliability and the associated uncertainty in the prior data processing. The model is tested on the propulsion engine of an ocean going vessel to forecast the likelihood of failure based on the logical dependencies among failure causative factors. Two scenarios were analyzed based on canonical probabilistic algorithms, and the results show that upon evidence on the three critical failure modes, the ship propulsion engine failure likelihood increased by 11.8%, 8.2%, and 9.4%, respectively. The model shows an adaptive/dynamic capability to capture new failure information and update the system’s failure probability. The proposed approach provides a condition monitoring tool and early warning guide for integrity management of critical ship energy systems. Received: 31 October 2022 | Revised: 19 January 2023 | Accepted: 20 February 2023 Conflicts of Interest The authors declare that they have no conflicts of interest to this work.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| 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) | 0.000 | 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