State prediction for marine diesel engine based on variational modal decomposition and long short-term memory. (November 2021)

• Record Type: Journal Article
• Authors: Qu, Chong; Zhou, Zhiguo; Liu, Zhiwen; Jia, Shuli; Wang, Lianfang; Ma, Liyong
• Is Part Of: Energy reports. Volume 7(2021)Supplement 7
• Page Start: 880
• Page End: 886
• Publication Date: 2021-11
• Subjects: Marine diesel engine -- State prediction -- Variational mode decomposition -- Long short-term memory; Power resources -- Periodicals; Energy industries -- Periodicals; Power resources; Periodicals; Electronic journals; 621.04205
• DOI: 10.1016/j.egyr.2021.09.185
• Languages: English
• ISSNs: 2352-4847

Abstract

Abstract: With the development of unmanned systems, more and more attentions are paid to the energy and power systems of data-driven ships. The autonomy of unmanned ships puts forward urgent requirements for the monitoring and prediction of the energy and power system of ships. Aiming at the state prediction for marine diesel engine, an improvement method based on variational modal decomposition (VMD) and long short-term memory (LSTM) is proposed in this paper. The sub signals are obtained by decomposing the signal to be predicted through VMD, the sub signals and resident signal are all predicted with LSTM, and the reconstruction prediction signal is obtained by sum all the predicted sub signals and resident signal. Compared with LSTM, ESN, and SVR methods, the proposed method reduces the prediction errors significantly. Compared with LSTM, the RE errors of the two sensors are reduced by 49.79% and 56.32% respectively, and the RMSE errors are reduced by 34.65% and 27.71% respectively. The performance of this method is better than other methods, and it has sufficient accuracy performance for state prediction of marine diesel engine.