Signal Processing and Fault Diagnosis on Structure Vibration Measurement Using a New Composed Deep Learning Model

Abstract

To protect offshore jacket platforms, it is essential to carry out the structure fault diagnosis. This paper proposes a new approach to identify structural faults in offshore jacket platforms, which is based on the integration of structural vibration data, a sophisticated data fusion process, and an intelligent diagnosis algorithm. In this new approach, firstly, the TCN is adopted to solve the efficiency bottleneck of the traditional recurrent neural networks in long time series signals, and significantly enhances the ability to capture long-distance dependent features in the structural response signals through the introduction of a dilated convolutional structure. Secondly, the bidirectional gated recurrent unit (BiGRU) network fuses forward and reverse gated recurrent units, which can effectively capture the forward and backward correlated timing features in the structure vibration data. The attention mechanism then further weights and optimises the BiGRU timing outputs so that the model can automatically focus on the signal pattern that is most discriminative for the fault identification. Furthermore, the artificial lemming algorithm (ALA) is used to optimise the hyperparameters of the TCN and BiGRU to improve the model efficiency and avoid the local optimum during the model training, thereby enhancing the generalisation performance of the proposed model. The validity of the proposed ALA-TCN- BiGRU model is substantiated through simulation and experimental validation. The results indicate that the proposed model can achieve an overall detection accuracy of over 98 % for the jacket structure, which is superior to several popular diagnosis methods.