Classification of fuel type for predictive maintenance in marine and industrial engines using time series feature extraction based on hypothesis tests and automated machine learning
Bibliographic record
Abstract
Predictive maintenance in internal combustion engines can be enhanced by accurately identifying the fuel type based on data collected from sensors or electronic control units (ECUs). This paper presents a study that aims to predict the fuel type (HVO100 or EN590) using machine learning techniques, specifically based only on the engine's rotational speed. The rotational speed data of a heavy-duty 6-cylinder diesel engine is measured and downsampled to frequencies of 100, 1000, and 10,000 Hz. To extract relevant features from the time series data, hundreds of features are extracted using hypothesis tests via the tsfresh library. Subsequently, selected features are trained using Databricks' automated machine learning (AutoML) platform. The study explores the relationships between the number of features, downsampling frequency, and the choice of machine learning models. The results indicate that, under the current configuration, the best test F1 score of 0.995 is achieved using logistic regression with 20 features and a downsampling frequency of 10,000 Hz. The analysis of SHAP values and p-values revealed that components of the Fourier transform and wavelet transform of the rotational speed play crucial roles in distinguishing between the fuel types. It is our hypothesis that the differences observed in the frequency domain are related to variations in fuel characteristics. Overall, this study presents a simple, interpretable, and computationally cost-efficient machine learning solution for predicting fuel type in industrial engines. The findings demonstrate the potential of applying this approach in real-world production environments.
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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) | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".