Calculation of Failure Detection Probability on Safety Mechanisms of Correlated Sensor Signals According to ISO 26262
Bibliographic record
Abstract
Functional safe products conforming to the ISO 26262 standard are getting more important for automotive applications wherein electronic takes more and more response for safety relevant operations. Consequently safety mechanisms are needed and implemented in order to reach defined functional safety targets. To prove their effectiveness diagnostic coverage provides a measurable quantity. A straight forward safety mechanism for sensor systems can be established by redundant signal paths measuring the same physical quantity and subsequently performing an independent output difference-check that decides if the data can be transmitted or an error message shall be sent. This paper focuses on the diagnostic coverage figure calculation of such data correlation-checks for linear sensors which are also shown in ISO 26262 part5:2011 ANNEX D2.10.2. This principle is widely used in automotive application like magnetic field sensing for steering-torque measurement, position detection of throttle-valve or current sensing of actuators. It is shown how the diagnostic coverage figures can be derived based on statistical evaluation using different typical failure-probability distributions of the analog and digital elements of the sensor systems. The incorporation of temperature and lifetime drifts that are part of the specification of all mixed signal systems and their influence on the diagnostic coverage will be discussed for different configurations of the safety requirements. These diagnostic coverage figures are essential for quantitative Failure-Tree Analysis (FTA) or Failure Mode, Effect and Diagnostic Analysis (FMEDA), which is the proof of the system capability to achieve its safety requirements.
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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 | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".