Analysis of Subsea Energy Supply Systems for Improved Maintenance Using ANFIS and TOPSIS

Abstract

The maintenance plan for the subsea energy supply system during the operation was optimized by firstly training the Adaptive Neuro-Fuzzy Inference System (ANFIS) model with historical data of process variables of the system such as voltage, current, power, and pressure in MATLAB software and then predicting the optimum output of the process using the trained model, which showed a good prediction of operational data after two cycles of computational analysis. The outputs from the trained model, coupled with expert opinions on historical data, were used to develop a Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) multi-criteria algorithm to select the best maintenance strategy. The reliability-centred maintenance, with a performance score of 0.811, ranked best amongst the maintenance strategies under the studied scenario. The result shows that the procedure could be applied in condition monitoring of operational subsea energy supply systems to predict impending faults through deviation error and prevent failure by the application of an appropriate maintenance strategy.

Keywords: ANFIS, TOPSIS, Maintenance plan, Fault prediction and Subsea power supply systems

DOI: 10.7176/ISDE/12-2-02

Publication date: April 30^th 2021