NASA Electrolytic Capacitor Degradation Data for Prognostics

NASA's dataset provides experimental data from an accelerated aging test on a set of identical electrolytic capacitors subjected to electrical overstress. The data forms the basis for developing a Kalman-filter-based algorithm to predict the component's remaining useful life. This case study supports a model-driven prognostics methodology for these critical, low-reliability electrical components.

Use Cases

• Developing remaining useful life (RUL) prediction algorithms based on capacitor degradation parameters.
• Validating empirical state-based degradation models using accelerated aging test results.
• Training and testing Kalman filter approaches for component-level prognostics.
• Assessing capacitor health based on current state and anticipated future operational conditions.

Strengths

• Dataset originates from NASA, a highly authoritative source for aerospace and engineering research.
• Data is derived from a controlled accelerated aging test on a set of identical capacitors, providing a basis for model development.
• Cross-platform presence on Data.gov signals its established use and importance in the field.

Limitations

• Critical metadata is missing: no column names, row counts, file sizes, or specific license information are provided.
• The dataset description is repeated verbatim across entries, suggesting sparse documentation.
• The 'last updated' date of 2026 is in the future, indicating a metadata error that casts doubt on freshness tracking.

Provenance

Source

National Aeronautics and Space Administration (NASA)

Collection Method

Accelerated aging test performed on a set of identical electrolytic capacitors subjected to electrical overstress.

Time Range

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Freshness

2026-04-10 (Note: This date is in the future, indicating a likely metadata error.)

Geography

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