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 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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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 teacher head, 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".