Damage detection of jacket platforms through improved stacked autoencoder and softmax classifier

Abstract

The jacket platform is a common structural form for offshore operations, and its health status determines the safety of the marine development process. In addressing the issue of damage detection in jacket platforms, a damage detection and prediction model based on stacked autoencoder (SAE) and softmax classifier is proposed. The model first constructs a SAE model using a layer-wise greedy training strategy to extract features from input signals. Subsequently, the extracted features are input into the softmax classifier for detecting damage types in the jacket platform. The model also utilizes a risk coefficient to forecast potential faults on the jacket platform. Experimental results on the jacket platform model demonstrate that the proposed method achieves an overall accuracy of 96.7% in damage detection in both isolated noise and marine environments, which is an improvement of approximately 5% compared to the autoencoder (AE) model. Additionally, in the damage prediction experiments, the model achieves a precision of 98.7% and a recall of 77.5% for potential damage.