Early State Prediction Model for Offshore Jacket Platform Structural Using EfficientNet-B0 Neural Network

Abstract

Offshore Jacket Platforms (OJPs) are often affected by environmental components that lead to damage, and the early detection system can help prevent serious failures, ensuring safe operations and mining conditions, and reducing maintenance costs. In this study, we proposed a prediction model based on Convolutional Neural Networks (CNNs) aimed at determining the early stage of the OJP structure’s abnormal status. Additionally, the EfficientNet-B0 Deep Neural Network classifies normal and abnormal states, which may cause problems, by using displacement signal analysis at specific areas taken into account throughout the test. Displacement data is transferred to a 2D scalogram image by applying a continuous Wavelet converter that shows the state of the work. Finally, the scalogram image data set is used as the input of the neural network, and feasibility experimental results compared with other typical neural networks such as GoogLeNet and ResNet-50 have verified the effectiveness of the approach.

Keywords

• Identify damage
• Offshore Jacket Platforms
• Vibration assessment
• Wavelet transformer
• CNN
• Confusion matrix