Cooling Fan Failure Modes to Enable Development of Automotive ECU Fan Health Monitoring System
Bibliographic record
Abstract
Electronic control units (ECUs) are widely used in the automotive industry. Recent efforts to enable enhanced and automated driving requires these ECUs to process and execute computationally expensive algorithms. With these developments, the ECUs now have a higher computing power and thus are at a greater risk of overheating. This may limit the availability of the essential functionalities in the vehicle. Currently, high operating temperatures are mitigated using passive cooling, which allows heat to dissipate without expelling any energy; however, more robust methods are required to enable this new technology. A cooling fan system is one of the desired methods for ECU thermal management, as this type of system draws cooler air from outside and expels the warm air from within. Therefore, the fan health status is critical to ensure ECU availability and reliability for vehicle operation, as when the fans become degraded, they cannot maintain the required airflow to minimize the ECU operating temperature. Traditionally, fan failures are detected by monitoring the fan speed versus the commanded duty cycle, thus it is desired to develop a robust health monitoring method for the fans. Fan failure mode study and fault injection can be used to enable the development of prognostics. Investigating the fan failure modes results in two main categories, which are internal and external. External fan failures include degradation and cracking of the outer casing, while internal failures include motor and ball bearing issues. Fault injection methods were developed based on these failure modes while considering potential operating conditions. For example, the fans were exposed to multiple environmental conditions, such as dust, humidity, and heat. These conditions can potentially trigger both internal and external failures. The data collection was conducted with the fans running in a standalone setup, being controlled by external equipment to ensure that the electronic input values were known. After running tests for 30 days, sufficient data was collected to enable degradation modelling. The data will contribute to the development of a predictive algorithm which will estimate the state of health of the fan based on its performance over time. This paper will discuss the failure modes and the data generated through simulation and fault injection.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".