Fault Detection and Diagnosis of Engine Spark Plugs Using Deep Learning Techniques
Bibliographic record
Abstract
<div>Fault Detection and Diagnosis (FDD) is playing an increasingly important role in the automotive sector as it moves toward Advanced Technology Vehicles. Reducing the cost of sensory equipment to detect faults in Internal Combustion Engines (ICEs) has always been a common desire for automotive researchers. This article offers an Artificial Intelligence approach for detecting engine combustion faults related to spark plugs using existing sensors. The study investigates two deep learning models that are capable of learning different fault conditions from historical sensory data. The two customized models, one Long Short-Term Memory (LSTM) neural network and one Convolutional Neural Networks (CNN) model, are proposed to tackle this task. The LSTM model processes the filtered sensor data in time series, while the CNN model uses the frequency map that is novel in the learning-based engine diagnosis field. A comprehensive engine fault dataset is collected and includes a variety of operating conditions in relation to engine speed, engine load, and test time. Evaluation results using this dataset show successful detection of the fault conditions with high accuracy. In the meantime, the results also reveal some unstable performance outside of given operating conditions.</div>
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 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".