What question did this study set out to answer?

The aim is to develop an AI-driven digital twin framework for predicting faults and remaining useful life in pressure vessels using synthetic acoustic emission data.

January 18, 2026Journal of Intelligent Manufacturing and Special EquipmentOpen Access

A physics-informed intelligent digital twin using multi-task CNN–LSTM for acoustic emission-based fault prognostics in safety-critical systems

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The aim is to develop an AI-driven digital twin framework for predicting faults and remaining useful life in pressure vessels using synthetic acoustic emission data.
Developed a multi-task CNN-LSTM architecture for analyzing synthetic acoustic emission waveforms.
Generated AE signals using a physics-informed model to represent material degradation.
Implemented the framework completely in Python without external simulations.
Utilized model convergence and residual analysis for robustness assessment.
Achieved 99% classification accuracy for health-state identification.
Maintained a low root mean square error for remaining useful life prediction (~5).
Visualizations showed distinct health-state clusters, enabling interpretable degradation learning.

Authors

AKAswin KarkadakattilSt. Peter's Institute of Higher Education and Research

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Implication

Demonstrates a digital twin framework predicting fault health and lifespan in safety-critical systems, suggesting a more efficient approach to diagnostics.

Cite This Study

Aswin Karkadakattil (2026) studied this question.

synapsesocial.com/papers/696c77afeb60fb80d1395eee https://doi.org/10.1108/jimse-10-2025-0023

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Implication

Demonstrates a digital twin framework predicting fault health and lifespan in safety-critical systems, suggesting a more efficient approach to diagnostics.

A physics-informed intelligent digital twin using multi-task CNN–LSTM for acoustic emission-based fault prognostics in safety-critical systems

Key Points

Discussion

Implication

Cite This Study

A physics-informed intelligent digital twin using multi-task CNN–LSTM for acoustic emission-based fault prognostics in safety-critical systems

Key Points

Discussion

Implication

Cite This Study

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