A new mooring failure detection approach based on hybrid LSTM-SVM model for semi-submersible platform

Abstract

A reliable and stable mooring system is of vital significance to warrant the continuous operation and personnel heath on offshore floating platforms. The failure detection technique for the mooring line can provide an early warning and response for people so that action can be taken earlier to avoid more severe consequences. This paper proposed a novel approach based on Long short-term memory neural networks and support vector machine (LSTM-SVM) to detect the mooring line failure. Two combined failure signals can judge the actual mooring failure occurrence. The preliminary mooring failure signals can be identified based on the significant increase of errors in the LSTM prediction of three platform motions. Besides, a SVM classifier is constructed based on three parallel sea state lines to distinguish the impact of motions between sea state change and mooring failure and capture the secondary failure signal. Five time-domain features are extracted from heave motion, and well-trained SVM model with 132 group cases data from three sea state lines can achieve characteristic recognition of sea state change and mooring failure. According to four new cases with continuously changing sea states, the proposed method indicates the feasibility of mooring failure detection when platforms suffer the influence of rapid environmental changes under a broader range of sea states, and the LSTM-SVM can still distinguish the sea state change and actual mooring failure. In this study, motion data acquisition relies on the hydrodynamic simulation of a semi-submersible platform (SEMI). For practical application, the proposed method can utilize the mature Differential Global Positioning System (DGPS) to obtain the data and realize the mooring failure detection in real time.