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, 2026Open Access

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

Key Points

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.

Abstract

Purpose Pressure vessels are vital components in manufacturing and energy systems, where early detection of degradation is essential to prevent catastrophic failures. Conventional acoustic emission (AE)-based prognostic methods depend heavily on experimental testing and finite element simulations, which are costly and time-intensive. This study proposes a fully computational, artificial intelligence (AI)-driven digital twin framework capable of performing both health-state classification and remaining useful life (RUL) prediction using physics-inspired synthetic AE data. Design/methodology/approach A multi-task convolutional neural network–long short-term memory (CNN–LSTM) architecture was developed to extract spatio-temporal features from synthetically generated AE waveforms. The AE signals were produced through a parameterized, physics-informed model representing energy attenuation and frequency variation during progressive material degradation. CNN layers capture spectral energy features, while LSTM layers learn temporal evolution patterns associated with structural deterioration. The framework, implemented entirely in Python, enables an end-to-end digital twin without external simulation platforms. Model convergence, residual analysis and t-distributed stochastic neighbor embedding visualizations were used to assess robustness and interpretability. Findings The model achieved 99% classification accuracy and a low RUL prediction error (root mean square error ˜ 5), demonstrating strong predictive capability and stable convergence. Latent-space visualization revealed distinct health-state clusters, validating interpretable degradation learning across synthetic AE data. Originality/value This work presents one of the first physics-informed, Python-based digital twin frameworks for AE-driven prognostics of pressure vessels. It eliminates dependence on costly experiments or finite element simulations, offering a scalable, data-efficient approach suitable for Industry 4.0 applications.

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

Key Points

Abstract

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