A general model-based prognostics methodology using particle filters is presented, with a focus on fixed-lag filters to improve prediction accuracy and precision. The approach is illustrated using a detailed physics-based model of a pneumatic valve and comprehensive simulation experiments. The dataset is provided by the National Aeronautics and Space Administration and was last updated on 2026-03-13.
Use Cases
- Developing prognostics algorithms based on physics-based models and particle filters.
- Evaluating the performance of fixed-lag filters for uncertainty management in prognostics.
- Simulating failure modes and remaining useful life predictions for pneumatic valves.
Strengths
- Methodology is based on detailed physics-based models derived from first principles.
- Prognostics performance is evaluated using specific metrics mentioned in the description.
- Source is the authoritative National Aeronautics and Space Administration.
Limitations
- Column-level documentation is absent; field semantics must be inferred after download.
- Row count and dataset size are unknown, which may limit suitability assessment.
- Data format is HTML, which may require parsing to extract structured data.
Provenance
- Source
- National Aeronautics and Space Administration
- Collection Method
- Simulation experiments based on a physics-based model.
- Time Range
- null
- Freshness
- Last updated 2026-03-13 20:32:03.843386; freshness should be verified.
- Geography
- null