Tracking Sensor Measurement Errors in Alfa Laval’s Pureballast Machines
Bibliographic record
Abstract
The maintenance of sensors in Alfa Laval’s PureBallast 3 ballast water treatment system presents significant challenges, particularly due to the unpredictable degradation of sensor accuracy over time. This thesis explores the use of unsupervised methodologies to detect and analyze sensor measurement errors, aiming to enhance operational efficiency and safety while reducing maintenance costs. Using sensor data from 39 machines, the study focuses on the UV Light Intensity(ULI) and Flow Transmitter (FT) sensors, leveraging exploratory data analysis, correlation analysis, the Mann-Kendall Tau test, and LOESS smoothing to identify trends indicative of sensor degradation. The results demonstrate that trend analysis can effectively uncover sensor degradation issues, providing statistically significant evidence for these trends. By comparing the behaviour of potentially faulty sensors to those of healthy ones, this research highlights the potential for a condition-based maintenance strategy, which could offer operational and economic benefits. Despite limitations such as the reliance on correlation analysis and the absence of labelled data, the study sets a new standard in the maintenance of ballast water treatment systems, ensuring safer maritime operations and preserving the integrity of marine ecosystems.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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 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".