Anomaly detection model of mooring system based on LSTM PCA method

Abstract

Aiming at the difficulty of damage detection of the mooring system, this paper proposes an anomaly detection framework combined with statistical analysis and neural network methods. Specifically, based on the real design parameters of a semi-submersible platform, a hydrodynamic model was established. Taking the damage degree of 5%, 10%, 15%, and 20% as examples, the analysis of the responses was carried out when the anchor chain was damaged in different positions. And then, based on the Long Short-Term Memory (LSTM) network and normal state data, a joint prediction model of the platform's six-degree-of-freedom (6DoF) motion was established considering the marine environment loads. The motion prediction error was only 0.0078. The Principle Component Analysis (PCA) together with the prediction residual sequences was tailored and employed to build the anomaly detection model. The Upper Confident Level (UCL) of the model was also given. The recognition accuracy reached 100% which can guide the safe service of the platform.