Counterfactual explanations for remaining useful life estimation within a Bayesian framework
| Main Author: | |
|---|---|
| Publication Date: | 2025 |
| Other Authors: | , |
| Format: | Article |
| Language: | eng |
| Source: | Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) |
| Download full: | http://hdl.handle.net/10362/178770 |
Summary: | Andringa, J., Baptista, M. L., & Santos, B. F. (2025). Counterfactual explanations for remaining useful life estimation within a Bayesian framework. Information Fusion, Article 102972. https://doi.org/10.1016/j.inffus.2025.102972 --- This work was supported by national funds through FCT (Fundação para a Ciência e a Tecnologia), under the project - UIDB/04152/2020 - Centro de Investigação em Gestão de Informação (MagIC)/NOVA IMS) (https://doi.org/10.54499/UIDB/04152/2020) |
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Counterfactual explanations for remaining useful life estimation within a Bayesian frameworkExplainable Artificial IntelligencePrognostics and Health ManagementC-MAPSS datasetBayesian uncertaintyCounterfactual explanationsInterpretability and explainabilityData augmentationSoftwareSignal ProcessingInformation SystemsHardware and ArchitectureAndringa, J., Baptista, M. L., & Santos, B. F. (2025). Counterfactual explanations for remaining useful life estimation within a Bayesian framework. Information Fusion, Article 102972. https://doi.org/10.1016/j.inffus.2025.102972 --- This work was supported by national funds through FCT (Fundação para a Ciência e a Tecnologia), under the project - UIDB/04152/2020 - Centro de Investigação em Gestão de Informação (MagIC)/NOVA IMS) (https://doi.org/10.54499/UIDB/04152/2020)Machine learning has contributed to the advancement of maintenance in many industries, including aviation. In recent years, many neural network models have been proposed to address the problems of failure identification and estimating the remaining useful life (RUL). Nevertheless, the black-box nature of neural networks often limits their transparency and interpretability. Interpretability (or explainability) in maintenance refers to the ability of a predictive model to provide insights into its decision-making process for predicting failures or estimating metrics like RUL. Counterfactual Explanations (CFEs) from Explainable AI (XAI) addresses this problem by explaining model decisions through hypothetical scenarios leading to alternative outcomes. A kind of neural network that could benefit from increased interpretability is Bayesian networks. In general, Bayesian models improve interpretability by quantifying uncertainty. However, incorporating Bayesian uncertainty into neural networks adds complexity because we often need a statistical distribution for each network parameter. This study investigates the use of CFEs within a Bayesian framework to achieve two key objectives simultaneously: (1) enhance the interpretability of RUL estimations and (2) improve model accuracy. We generate two types of CFEs: (1) RUL CFEs that increase/decrease the RUL estimation and (2) uncertainty CFEs with reduced estimation uncertainty, which we use to augment the dataset and increase model accuracy. We apply this method to a classical case study, the C-MAPSS dataset, using a Bayesian Long Short-Term Memory (B-LSTM) model. We demonstrate that CFEs can help identify critical features and fine-tune corrective actions to achieve specific outcomes. For example, following a maintenance action that increased the temperature by 1°F, CFEs can reveal that this adjustment extended the equipment’s useful life by 30 cycles. This ability to correlate specific actions with effects enhances both decision-making and maintenance efficiency. Additionally, our data augmentation approach results in a 5% improvement in accuracy for a strict of 20%. The root mean square error (RMSE) of the B-LSTM model decreases from 9.56 to 8.47 cycles, demonstrating the potential of Uncertainty CFEs to improve accuracy in aircraft maintenance. The code is publicly available at Github.Information Management Research Center (MagIC) - NOVA Information Management SchoolNOVA Information Management School (NOVA IMS)RUNAndringa, JillesBaptista, Márcia L.Santos, Bruno F.2025-02-10T21:18:57Z2025-062025-06-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article15application/pdfhttp://hdl.handle.net/10362/178770eng1566-2535PURE: 108627584https://doi.org/10.1016/j.inffus.2025.102972info:eu-repo/semantics/openAccessreponame:Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)instname:FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiainstacron:RCAAP2025-03-31T01:54:59Zoai:run.unl.pt:10362/178770Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireinfo@rcaap.ptopendoar:https://opendoar.ac.uk/repository/71602025-05-28T20:39:31.291193Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) - FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiafalse |
| dc.title.none.fl_str_mv |
Counterfactual explanations for remaining useful life estimation within a Bayesian framework |
| title |
Counterfactual explanations for remaining useful life estimation within a Bayesian framework |
| spellingShingle |
Counterfactual explanations for remaining useful life estimation within a Bayesian framework Andringa, Jilles Explainable Artificial Intelligence Prognostics and Health Management C-MAPSS dataset Bayesian uncertainty Counterfactual explanations Interpretability and explainability Data augmentation Software Signal Processing Information Systems Hardware and Architecture |
| title_short |
Counterfactual explanations for remaining useful life estimation within a Bayesian framework |
| title_full |
Counterfactual explanations for remaining useful life estimation within a Bayesian framework |
| title_fullStr |
Counterfactual explanations for remaining useful life estimation within a Bayesian framework |
| title_full_unstemmed |
Counterfactual explanations for remaining useful life estimation within a Bayesian framework |
| title_sort |
Counterfactual explanations for remaining useful life estimation within a Bayesian framework |
| author |
Andringa, Jilles |
| author_facet |
Andringa, Jilles Baptista, Márcia L. Santos, Bruno F. |
| author_role |
author |
| author2 |
Baptista, Márcia L. Santos, Bruno F. |
| author2_role |
author author |
| dc.contributor.none.fl_str_mv |
Information Management Research Center (MagIC) - NOVA Information Management School NOVA Information Management School (NOVA IMS) RUN |
| dc.contributor.author.fl_str_mv |
Andringa, Jilles Baptista, Márcia L. Santos, Bruno F. |
| dc.subject.por.fl_str_mv |
Explainable Artificial Intelligence Prognostics and Health Management C-MAPSS dataset Bayesian uncertainty Counterfactual explanations Interpretability and explainability Data augmentation Software Signal Processing Information Systems Hardware and Architecture |
| topic |
Explainable Artificial Intelligence Prognostics and Health Management C-MAPSS dataset Bayesian uncertainty Counterfactual explanations Interpretability and explainability Data augmentation Software Signal Processing Information Systems Hardware and Architecture |
| description |
Andringa, J., Baptista, M. L., & Santos, B. F. (2025). Counterfactual explanations for remaining useful life estimation within a Bayesian framework. Information Fusion, Article 102972. https://doi.org/10.1016/j.inffus.2025.102972 --- This work was supported by national funds through FCT (Fundação para a Ciência e a Tecnologia), under the project - UIDB/04152/2020 - Centro de Investigação em Gestão de Informação (MagIC)/NOVA IMS) (https://doi.org/10.54499/UIDB/04152/2020) |
| publishDate |
2025 |
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2025-02-10T21:18:57Z 2025-06 2025-06-01T00:00:00Z |
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info:eu-repo/semantics/publishedVersion |
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info:eu-repo/semantics/article |
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article |
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http://hdl.handle.net/10362/178770 |
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eng |
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eng |
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1566-2535 PURE: 108627584 https://doi.org/10.1016/j.inffus.2025.102972 |
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15 application/pdf |
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