Glossary Updates12 new terms added to the glossaries · October 2, 2026, 22:44 CEST
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Glossary · Data, timing and communication

Plausibility check

Also known as: plausibility monitoring

In functional safety and systems engineering, a plausibility check is a rule-based check that evaluates whether a value or state is credible in context, for example by comparing it with physical limits, rates of change or related signals.

  • Functional safety
  • Data and interfaces

In one sentence

A plausibility check asks whether a value makes sense in context; it is a key diagnostic for sensors, signals and data exchanged between systems.

Example

A crane controller flags a load reading of 12 t on a 5 t hoist, or a conveyor speed that jumps from 0 to 3 m/s within one cycle, as implausible.

How it applies

  • Functional safety: Plausibility checks are a common diagnostic measure, for example comparing two redundant channels or a speed signal with the commanded direction. They contribute to fault detection only if a defined fault reaction follows.
  • Integrated systems: Define the rules from physics and process knowledge: limits, maximum rate of change, consistency between related signals and consistency with the current state model.
  • Limits: A plausible value can still be wrong. Slow sensor drift stays within plausible limits, so plausibility checks complement calibration but do not replace it.

Plausibility check vs. data validation

Data validation covers all rules for structure, meaning, range, timing and quality. A plausibility check is the part that asks whether a correctly formed value is credible in its context.

By knowledge.aitechdoc.world · Published September 25, 2026 · Last reviewed

Source: AI TechDoc Knowledge editorial definition, based on systems engineering practice

Definitions follow the cited standards and specifications. Where a source is a copyrighted publication, such as an ISO, IEC or EN standard, the definition is a close paraphrase, not a verbatim quotation, so as not to infringe copyright. We recommend reading the original publication. The sections “How it applies” are editorial commentary by AI TechDoc Knowledge and are not part of any standard.

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