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

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.