Assessing the Unreliability of Systems during the Early Operation Period of a Ship—A Case Study
Bibliographic record
Abstract
Sea-going ships are unique systems, and each ship—even those which are mass-produced—are different. Once in service, they are subjected to unique environmental exposure due to a variety of factors, including, but not limited to, their mode of operation, sailing area, cargo, hydrometeorological conditions, crew training, etc. This makes it very difficult, if not impossible, to compare individual units. The aim of this study is to present the damage data and analysis of a selected vessel—a complex technical system—during its first year of operation. To that end, the paper analyses the unreliability of a bulk cargo ship’s technical and energetic system components during its first year of operation. The paper also introduces the failure susceptibility of its technical systems, defines concepts of wear and failure and describes the object of analysis. Observed failures in subsystem components of the marine power plant, in the general systems and in the technological system of the ship, were presented in tabular form. Each failure was described by considering the time of operation until the first failure, type of failure, type of wear, nature of an event and methods used to regain efficiency. Selected failures were described in great detail, and the statistics of the ship’s components’ susceptibility to failure were presented by considering the wear type that caused a failure, the component type and the time to the first failure. Additionally the severity of each failure is discussed. Finally, conclusions regarding the susceptibility to failure of particular ship components were 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.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".