A hybrid human reliability assessment technique for the maintenance operations of marine and offshore systems
Why this work is in the frame
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Bibliographic record
Abstract
Abstract Regular maintenance is very important to ensure all the required types of machinery and equipment be kept 100% efficient for marine and offshore systems. Maintenance operations for marine and offshore systems are carried out by the seafarers/operators and it is they who are usually liable for any error. Previous studies have identified that about 80% of marine and offshore accidents occurred due to human error. Therefore, to address this concern human reliability assessment (HRA) is very important. However, an appropriate technique is required to estimate human error probability (HEP) for marine and offshore systems. Human error assessment and reduction technique (HEART) is applied to many industries to determine HEP. Recently, HEART was specifically developed for the maintenance operations of marine and offshore systems considering marine and offshore environmental and operational conditions. However, there is a deficiency in this technique as it does not provide a concrete method to determine the seafarers assessed proportion of effect (SAPOE) and it therefore, relies heavily on the judgment of a single expert. This study proposes a hybrid HEART to overcome the problem. The hybrid HEART utilizes the evidence theory to fuse an expert's opinion to determine APOA for each corresponding error producing condition (EPC). The proposed technique is applied to estimate HEP for the maintenance procedures of a condensate pump for an offshore oil and gas facility as a case study. The HEP values are calculated for each selected activity and comparison is provided. Based on the results, a performing pressure test and isolation leak test has the highest HEP 1.54E‐01 and depressurizing drain lines has the lowest 1.54E‐04. It is proposed that the application of this hybrid HEART will enable estimating HEP more accurately. Therefore, it will contribute to improving the overall safety level in the maintenance of marine and offshore systems.
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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 | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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