Sepideh Pashami

RISE

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Explainable AI and Decision Support in Industrial Maintenance

Lokal:
Teknikscenen

Combining survival-analysis and data-driven models for predicting remaining useful life

The objective of the approaches for predictive maintenance typically follow one of two paradigms: survival analysis, which considers cumulative wear and usage to capture long-term aging, and data-driven RUL estimation, which relies on time-series sensor measurements to assess the current health state and predict near-future degradation. In this work we show how these two approaches for predicting RUL can be combined.

Lokal:
Teknikscenen

The objective of the approaches for predictive maintenance typically follow one of two paradigms: survival analysis, which considers cumulative wear and usage to capture long-term aging, and data-driven RUL estimation, which relies on time-series sensor measurements to assess the current health state and predict near-future degradation. In this work we show how these two approaches for predicting RUL can be combined.