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Interpretable prognostics with concept bottleneck models
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DOI：10.1016/j.inffus.2025.103427.png)
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Abstract
En 中文
• Concept Bottleneck Models for interpretable deep learning-based RUL prediction using degradation modes as human-understandable concepts. • Evaluation and comparison of several black-box and concept-based models on N-CMAPSS datasets. • Adaptation of test-time interventions for prognostics applications. • Concept Embedding Models achieve both high performance and interpretability even with limited available concepts.
Keywords:
Prognostics
Remaining useful life
Concept Bottleneck Model
Explainable AI
XAI
Interpretability
Concepts
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15.5
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4.3K
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2.7W
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