Enhancing structural condition assessment in steel pipelines via a WGAN-AAE data fusion methodology

• Introduced a novel WGAN-AAE framework for real-synthetic data fusion • Achieved 94.89% accuracy in multi-label pipeline condition classification • Demonstrated high sensitivity of FBG sensors to pressure and flow changes • Identified optimal sensor positions for improved signal and detection • Proposed a robust 2D-CNN architecture tailored for leakage classification Pipelines for water and oil transport are vulnerable to ageing, operational loads, and harsh environments, necessitating reliable condition monitoring. This study presents a hybrid sensing and data-driven framework for accurate leak detection and localisation in steel pipelines instrumented with Fibre Bragg Grating (FBG) sensors. For the research, a 6 m steel pipe was instrumented with FBG sensors placed longitudinally and circumferentially to quantify strain responses under various leakage and operating conditions. The study evaluates the sensitivities of the FBG strain recordings to variations in leak size, leak location, pressure and flow. A hybrid WGAN–AAE data fusion methodology is proposed to analyse limited and noisy strain data for accurate classification of leakages, flow rate and pressure. The framework integrates a Wasserstein GAN (WGAN) that generates synthetic FBG signals tuned to the operating envelope; an adversarial autoencoder (AAE), which provides domain-aware latent regularisation and learned feature-level fusion of real and synthetic data; and a 2D-Convolutional Neural Network classifier that operates on the fused representations. Models are trained on 70% real data augmented with WGAN samples and evaluated on the remaining 30% real data. The study addresses both single-label (pressure, flow, leak size, leak location) and multi-label classification based on a Binary Relevance approach. The classifiers achieved a 94.89% ± 1.13% accuracy for multi-label classification on the test set based on real data. Additionally, single classification tasks show high accuracy rates, with 91.04% ± 0.90% for flow rate classification, over 98.69% ± 0.93% for pressure classification, 100% ± 0.00% for leakage size classification, and 91.22% ± 0.98% for leakage localization. A comparative analysis of leakage-location classification across the ten best sensor layouts shows that WGAN+AAE outperforms GAN+AAE and WGAN-concatenation baselines, supporting the benefits of Wasserstein synthesis, latent regularisation, and learned fusion. These results demonstrate that the proposed hybrid sensing and data-fusion approach enables accurate and robust leak detection and localisation in pipeline systems, even with limited datasets and under noisy, variable operating conditions.