Additional study of failure behavior for ship propeller: The comparative analysis of life evaluation approaches between BP neural network, LSTM and TV-HSMM

Abstract

The study focuses on investigating the appropriate method for evaluating the residual life of a propeller and analyzing its fracture characteristics. A fluid-structure interaction simulation is conducted to qualitatively analyze the stress distribution of the propeller during operation, identifying regions prone to stress concentration. Subsequently, fracture surface characteristics are examined, revealing quasi-cleavage fracture features such as tongue striations, dimples, and tearing ridges in the initiation and propagation regions. Additionally, composite fracture characteristics and geometric mutations are observed on the edge of the fracture surface. Three residual life evaluation methods are then proposed, utilizing BP neural network, LSTM, and TV-HSMM. Comparative analysis suggests that the TV-HSMM method offers the advantage of minimal error in residual life evaluation. However, the models based on BP neural network and LSTM are preferred for their lower average error and distribution trends that closely resemble actual life data.