A Residual Convolutional Autoencoder-Based Structural Damage Detection Approach for Deep-Sea Mining Riser Considering Data Fusion

A deep-sea riser is a crucial component of the mining system used to lift seafloor mineral resources to the vessel. Even minor damage to the riser can lead to substantial financial losses, environmental impacts, and safety hazards. However, identifying modal parameters for structural health monitoring remains a major challenge due to its large deformations and flexibility. Vibration signal-based methods are essential for detecting damage and enabling timely maintenance to minimize losses. However, accurately extracting features from one-dimensional (1D) signals is often hindered by various environmental factors and measurement noises. To address this challenge, a novel approach based on a residual convolutional auto-encoder (RCAE) is proposed for detecting damage in deep-sea mining risers, incorporating a data fusion strategy. First, principal component analysis (PCA) is applied to reduce environmental fluctuations and fuse multisensor strain readings. Subsequently, a 1D-RCAE is used to extract damage-sensitive features (DSFs) from the fused dataset. A Mahalanobis distance indicator is established to compare the DSFs of the testing and healthy risers. The specific threshold for these distances is determined using the 3σ criterion, which is employed to assess whether damage has occurred in the testing riser. The effectiveness and robustness of the proposed approach are verified through numerical simulations of a 500-m riser and experimental tests on a 6-m riser. Moreover, the impact of contaminated noise and environmental fluctuations is examined. Results show that the proposed PCA-1D-RCAE approach can effectively detect damage and is resilient to measurement noise and environmental fluctuations. The accuracy exceeds 98% under noise-free conditions and remains above 90% even with 10 dB noise. This novel approach has the potential to establish a new standard for evaluating the health and integrity of risers during mining operations, thereby reducing the high costs and risks associated with failures. Maintenance activities can be scheduled more efficiently by enabling early and accurate detection of riser damage, minimizing downtime and avoiding catastrophic failures.