# Interpretable Prognostics with Concept Bottleneck Models

arXiv: https://arxiv.org/abs/2405.17575 · HTML: https://mlsanity.com/paper/2405.17575/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 28 of its 73 references that are in the map.
- First posted on arXiv 27 May 2024.

## Builds on (most cited first, 28 of 28)

- [Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI](https://mlsanity.com/paper/1910.10045/) (2019)
- [Interpretability Beyond Feature Attribution: Quantitative Testing with Concept Activation Vectors (TCAV)](https://mlsanity.com/paper/1711.11279/) (2017)
- [Concept Bottleneck Models](https://mlsanity.com/paper/2007.04612/) (2020)
- [The Mythos of Model Interpretability](https://mlsanity.com/paper/1606.03490/) (2016)
- [Interpretation of Neural Networks is Fragile](https://mlsanity.com/paper/1710.10547/) (2017)
- [The (Un)reliability of saliency methods](https://mlsanity.com/paper/1711.00867/) (2017)
- [A Survey on Neural Network Interpretability](https://mlsanity.com/paper/2012.14261/) (2020)
- [On Completeness-aware Concept-Based Explanations in Deep Neural Networks](https://mlsanity.com/paper/1910.07969/) (2019)
- [Promises and Pitfalls of Black-Box Concept Learning Models](https://mlsanity.com/paper/2106.13314/) (2021)
- [Potential, Challenges and Future Directions for Deep Learning in Prognostics and Health Management Applications](https://mlsanity.com/paper/2005.02144/) (2020)
- [Post hoc Explanations may be Ineffective for Detecting Unknown Spurious Correlation](https://mlsanity.com/paper/2212.04629/) (2022)
- [Interpretability Beyond Classification Output: Semantic Bottleneck Networks](https://mlsanity.com/paper/1907.10882/) (2019)
- [Does Explainable Artificial Intelligence Improve Human Decision-Making?](https://mlsanity.com/paper/2006.11194/) (2020)
- [Fusing Physics-based and Deep Learning Models for Prognostics](https://mlsanity.com/paper/2003.00732/) (2020)
- [Deep Supervision with Intermediate Concepts](https://mlsanity.com/paper/1801.03399/) (2018)
- [Explainable, Interpretable & Trustworthy AI for Intelligent Digital Twin: Case Study on Remaining Useful Life](https://mlsanity.com/paper/2301.06676/) (2023)
- [Dual Aspect Self-Attention based on Transformer for Remaining Useful Life Prediction](https://mlsanity.com/paper/2106.15842/) (2021)
- [Forecasting remaining useful life: Interpretable deep learning approach via variational Bayesian inferences](https://mlsanity.com/paper/1907.05146/) (2019)
- [Domain Adaptation via Alignment of Operation Profile for Remaining Useful Lifetime Prediction](https://mlsanity.com/paper/2302.01704/) (2023)
- [Semantic bottleneck for computer vision tasks](https://mlsanity.com/paper/1811.02234/) (2018)
- [Semantically Interpretable Activation Maps: what-where-how explanations within CNNs](https://mlsanity.com/paper/1909.08442/) (2019)
- [Explainable Predictive Maintenance: A Survey of Current Methods, Challenges and Opportunities](https://mlsanity.com/paper/2401.07871/) (2024)
- [Does Your Model Think Like an Engineer? Explainable AI for Bearing Fault Detection with Deep Learning](https://mlsanity.com/paper/2310.12967/) (2023)
- [An Explainable Regression Framework for Predicting Remaining Useful Life of Machines](https://mlsanity.com/paper/2204.13574/) (2022)
- [On the Soundness of XAI in Prognostics and Health Management (PHM)](https://mlsanity.com/paper/2303.05517/) (2023)
- [Semi-Supervised Health Index Monitoring with Feature Generation and Fusion](https://mlsanity.com/paper/2312.02867/) (2023)
- [Deep Koopman Operator-based degradation modelling](https://mlsanity.com/paper/2308.01690/) (2023)
- [An Explainable Deep Learning-based Prognostic Model for Rotating Machinery](https://mlsanity.com/paper/2004.13608/) (2020)

## Built on by (most cited first, 6 of 6)

- [Towards Reasonable Concept Bottleneck Models](https://mlsanity.com/paper/2506.05014/) (2025)
- [From Black Box to Biomarker: Sparse Autoencoders for Interpreting Speech Models of Parkinson's Disease](https://mlsanity.com/paper/2507.16836/) (2025)
- [Survival Concept-Based Learning Models](https://mlsanity.com/paper/2502.05950/) (2025)
- [Explainable AI guided unsupervised fault diagnostics for high-voltage circuit breakers](https://mlsanity.com/paper/2507.19168/) (2025)
- [When, How Long and How Much? Interpretable Neural Networks for Time Series Regression by Learning to Mask and Aggregate](https://mlsanity.com/paper/2512.03578/) (2025)
- [CruiseBench: A Real-Flight-Aligned N-CMAPSS Benchmark for Engine RUL Prediction](https://mlsanity.com/paper/2607.19380/) (2026)

## Abstract (arXiv)

Deep learning approaches have recently been extensively explored for the prognostics of industrial assets. However, they still suffer from a lack of interpretability, which hinders their adoption in safety-critical applications. To improve their trustworthiness, explainable AI (XAI) techniques have been applied in prognostics, primarily to quantify the importance of input variables for predicting the remaining useful life (RUL) using post-hoc attribution methods. In this work, we propose the application of Concept Bottleneck Models (CBMs), a family of inherently interpretable neural network architectures based on concept explanations, to the task of RUL prediction. Unlike attribution methods, which explain decisions in terms of low-level input features, concepts represent high-level information that is easily understandable by users. Moreover, once verified in actual applications, CBMs enable domain experts to intervene on the concept activations at test-time. We propose using the different degradation modes of an asset as intermediate concepts. Our case studies on the New Commercial Modular AeroPropulsion System Simulation (N-CMAPSS) aircraft engine dataset for RUL prediction demonstrate that the performance of CBMs can be on par or superior to black-box models, while being more interpretable, even when the available labeled concepts are limited. Code available at hrefhttps://github.com/EPFL-IMOS/concept-prognostics/urlgithub.com/EPFL-IMOS/concept-prognostics/.