Research on Intelligent Diagnosis of Sensor Faults on Pipe-Rack Offshore Platform Under Typhoon Action

As a key component for perception of the environment and structural response, the normal operation of sensors is crucial for the safety assessment of pipe-rack offshore platforms. However, in long-term service, sensors on the jacket platform are susceptible to high salt-spray corrosion and strong vibration in marine environments, which can lead to bias, drift, accuracy degradation, gain anomaly, and stuck-at faults. These fault signals can mask real structural damage and cause misjudgment. The existing temporal diagnosis models have difficulty in simultaneously capturing long-range dependencies and the local dynamic evolution in temporal data. Therefore, this paper proposes to construct a sensor fault diagnosis model based on the parallel fusion of a Temporal Convolutional Network (TCN) and Gated Recurrent Unit (GRU). First, five types of sensor fault mathematical models were established, and 10 composite fault conditions were constructed. Second, a dual branch parallel architecture was designed, using sliding windows and time-domain expansion to construct the time-series dataset. The TCN branch relies on causal and dilated convolution to capture the long-term dependencies of signals, while the GRU branch extracts local dynamic evolution features through a dual gate control mechanism. The TCN and GRU branches extract features in parallel and fuse them to complete fault classification. On a numerical model of a pipe-rack offshore platform containing seven triaxial acceleration sensors, this method achieved an accuracy of 97.34% in diagnosing 10 types of composite faults, significantly better than single TCN (89.2%) and single GRU (84.8%). The confusion matrix display can effectively distinguish even when encountering fault types with similar characteristics such as drift + stuck composite faults. In the scaled-model physical experiment, the TCN-GRU sensor fault diagnosis model is validated on a ten-class composite fault dataset constructed by superimposing five types of mathematically synthesized sensor faults onto measured healthy signals collected from a laboratory-scale model (not naturally occurring sensor faults), and achieves an accuracy of 91.18%. This study focuses on the sensor fault diagnosis of offshore platforms with pipeline racks under the influence of typhoons, and proposes an intelligent sensor fault diagnosis model based on TCN-GRU parallel fusion. At the same time, the model was validated through scaled physical experiments, as an auxiliary check to verify the effectiveness of the experimental platform and data-acquisition system.