Voltage-Based Physical Layer Fault Diagnosis for Controller Area Network
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Bibliographic record
Abstract
Controller Area Network (CAN) is the most prevalent communication protocol used in the automotive industry. This in-vehicle network provides a means communication between Electronic Control Units (ECUs) and components within the vehicle. The recent rapid development of connected, electric, and autonomous vehicles expands the complexity and information exchange within CAN and demands an increase in the reliability of the network. Efficient system-level diagnosis functions need to be integrated over the network to ensure for reliability and enhance the ease of troubleshooting.
 This paper presents a method to identify physical CAN faults such as loss of electrical connections and shorted wires. Fault signatures of predefined physical CAN faults are used to detect and identify the failure modes. The method can identify both permanent and intermittent faults caused by, for instance, damaged connectors and vibrations, respectively.
 Diagnosis tasks are implemented on in-vehicle module by measuring and processing physical layer voltages of all CAN buses. A real-time data buffer of a predefined size is utilized to calculate health indicators from the physical layer CAN voltages. The health indicators are then compared to predefined thresholds to determine the presence and type of the fault. Compared to ground truth data, the results show that the presented method can identify with high accuracy physical CAN faults including open electrical connection and shorted wires.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| 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