Summary of Interpretable Prognostics with Concept Bottleneck Models, by Florent Forest et al.
Interpretable Prognostics with Concept Bottleneck Models
by Florent Forest, Katharina Rombach, Olga Fink
First submitted to arxiv on: 27 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Signal Processing (eess.SP); Machine Learning (stat.ML)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Medium Difficulty Summary: Deep learning approaches have been extensively explored for industrial asset prognostics, but lack interpretability, hindering adoption in safety-critical applications. To improve trustworthiness, explainable AI (XAI) techniques were applied, primarily using post-hoc attribution methods to quantify input variable importance for remaining useful life (RUL) prediction. This work proposes applying Concept Bottleneck Models (CBMs), inherently interpretable neural network architectures based on concept explanations, to RUL prediction. Unlike attribution methods, concepts represent high-level information understandable by users. CBMs enable domain experts to intervene on concept activations at test-time. The proposed approach uses degradation modes of an asset as intermediate concepts. Case studies on the N-CMAPSS aircraft engine dataset demonstrate that CBMs can perform on par or superior to black-box models while being more interpretable, even with limited labeled concepts. Code available at https://github.com/EPFL-IMOS/concept-prognostics/. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Low Difficulty Summary: This paper is about making artificial intelligence (AI) more understandable and trustworthy for predicting the remaining useful life of machines like airplanes. Right now, AI models are not very good at explaining why they make certain predictions, which makes it hard to use them in important situations. The authors propose a new way of doing this using something called Concept Bottleneck Models. These models explain their decisions based on big-picture concepts that humans can understand, rather than just individual details. This makes the AI more reliable and easier for experts to use. The authors tested their approach on a dataset from an airplane engine company and found that it worked well even with limited information. |
Keywords
* Artificial intelligence * Deep learning * Neural network
