Zheliang Fan

Damage identification in offshore jacket structures requires state screening when damaged samples are scarce and local damage classification under measurement noise. We propose a framework that combines unsupervised screening with a lightweight bimodal classifier. An autoencoder learns undamaged response features from undamaged data alone and provides initial damage screening. The bimodal classifier combines acceleration time histories and mode-shape curvature difference (MSCD) images through bidirectional cross-modal attention and gated fusion. Beam simulations achieved 92.26 ± 1.41% accuracy at 15% Gaussian noise, exceeding direct concatenation by 3.63 percentage points. In scaled jacket tests, the autoencoder detected 98.58% of damaged samples. With 5% low-frequency colored perturbation, the bimodal classifier achieved 100% accuracy across seven structural states on a bootstrap-resampled test set. The classifier contains 3.44 million parameters and required approximately 12 ms per sample on the tested CPU.

The Soft Yoke Mooring (SYM) system is a critical single-point mooring method for Floating Production Storage and Offloading systems (FPSOs) in shallow waters. Its articulated thrust roller bearing operates long-term in harsh marine environments, making it prone to failure and difficult to diagnose. To address the issues of non-stationary signals and fault features submerged in strong noise caused by the bearing’s non-rotational oscillatory motion, this paper proposes an adaptive improved diagnosis scheme based on Minimum Entropy Deconvolution (MED). By optimizing Finite Impulse Response (FIR) filter parameters to adapt to the oscillatory operating conditions and combining joint analysis of time-domain indicators and envelope spectra, precise identification of bearing faults is achieved. Research shows that this method effectively enhances fault impact components. After MED processing, the kurtosis value of the fault signal can be significantly increased from approximately 2.6 to over 8.6. Its effectiveness in noisy environments was verified through simulation. Experiments conducted on a 1:10 scale soft yoke model demonstrated that the MED denoising and filtering signal analysis method can effectively identify damage in the thrust roller bearing of the SYM system under marine conditions characterized by high noise and complex frequencies. This study provides an efficient and reliable method for fault diagnosis of non-rotational oscillatory bearings in complex marine environments, holding significant engineering application value.