Journal article
Enhancing real-time remaining useful life prediction with information entropy uncertainty quantified deep learning models
Journal of intelligent material systems and structures, v 37(6)
08 Dec 2025
Abstract
Accurate prediction of the Remaining Useful Life (RUL) of critical components in industrial applications is essential for optimizing maintenance strategies and ensuring operational safety. Traditional methods often struggle with the complexities of real-time data integration and fail to provide uncertainty measures critical for high-stakes decision-making. This study introduces a novel deep learning framework that not only predicts RUL in real-time but also quantifies the uncertainty of these predictions, enhancing the reliability of the prognosis. To demonstrate this approach, neural network architectures were developed to understand real-time inputs of time-series type acoustic emission nondestructive evaluation data. The key innovation of the approach is the use of information entropy with window-analysis to analyze streaming data. The combination of window analysis and deep learning allows for autonomous analysis for the user. Furthermore, a digital thread was developed to predict RUL as data was streamed. To address the challenge of uncertainty, the model architectures incorporate Monte Carlo dropout, providing a probabilistic interpretation of the predictions. Experimental results demonstrate that the approach not only improves the accuracy of RUL estimates compared to traditional models but also offers meaningful uncertainty bounds which are vital for risk-averse industries.
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Details
- Title
- Enhancing real-time remaining useful life prediction with information entropy uncertainty quantified deep learning models
- Creators
- Sarah Malik - Drexel UniversityJesse Yochens - Ohio UniversityBrian Wisner - Ohio UniversityAntonios Kontsos (Corresponding Author) - Rowan University
- Publication Details
- Journal of intelligent material systems and structures, v 37(6)
- Publisher
- SAGE Publications
- Number of pages
- 16
- Grant note
- ASME Donald O. Thompson Graduate Nondestructive Evaluation Fellowship Graduate Areas of National Assistance (GAANN) fellowship National Science Foundation (NSF) through the NSF GRFP program
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Mechanical Engineering and Mechanics
- Web of Science ID
- WOS:001632407500001
- Scopus ID
- 2-s2.0-105024232141
- Other Identifier
- 991022146935504721
InCites Highlights
Data related to this publication, from InCites Benchmarking & Analytics tool:
- Collaboration types
- Domestic collaboration
- Web of Science research areas
- Materials Science, Multidisciplinary