RUL Prediction Performance Comparison Under Sensor Fault Conditions

Dongdong Tang published a 5.5 KB Excel file on figshare in April 2026. The dataset compares the performance of a proposed framework against representative deep learning models for predicting Remaining Useful Life (RUL) under sensor fault conditions. Evaluation metrics include RMSE, the standard C-MAPSS scoring function, and statistical significance.

Use Cases

• Benchmarking new RUL prediction algorithms based on the reported RMSE and C-MAPSS scores.
• Evaluating model robustness under sensor fault conditions as described in the dataset.
• Conducting statistical significance tests on RUL prediction performance using the provided data.

Strengths

• Dataset is small (5.5 KB), facilitating quick download and inspection.
• Performance is evaluated using multiple established metrics: RMSE and the standard C-MAPSS scoring function.
• Includes statistical significance analysis for the performance comparison.

Limitations

• Row count is unknown, which may limit suitability assessment.
• Column-level documentation is absent; field semantics must be inferred after download.
• Description metadata is limited; actual data quality requires manual inspection after download.

Provenance

Source

figshare

Freshness

Last updated 2026-04-29 17:37:18; freshness should be verified.