Study on bearing force of marine propeller induced by longitudinal vibration of propulsion-shafting

Zou, Donglin; Lv, Fangrui; Ta, Na; Rao, Zhushi

doi:10.1080/17445302.2019.1601813

Cited by 11 publications

(1 citation statement)

References 20 publications

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“…In the field related to ship propulsion shaft system bearings, studies in the literature [16][17][18] have used back propagation neural networks (BPNNs) and symmetrical dot pattern (SDP) visual diagnostic methods to diagnose the health of the ship propulsion shaft system and achieved satisfactory results.…”

Section: Introductionmentioning

confidence: 99%

Research on the Bearing Lifespan Prediction Method for Ship Propulsion Shaft Systems Based on an Enhanced Domain Adversarial Neural Network

Ren,

Du,

Chang

2023

JMSE

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To address the challenge of accurate lifespan prediction for bearings in different operating conditions within ship propulsion shaft systems, a two-stage prediction model based on an enhanced domain adversarial neural network (DANN) is proposed. Firstly, pre-training features containing comprehensive degradation information are extracted from the entire source domain dataset encompassing all operational conditions. Subsequently, DANN is employed to extract domain-invariant features that are difficult to distinguish. Following this, a feature alignment process is utilized to align high-dimensional features with pre-training features, thereby mitigating the adverse effects caused by missing data in the incomplete target operational condition dataset. Finally, the effectiveness of this approach is validated using operational data from bearings under multiple operating conditions. The experimental results demonstrate that the method presented in this paper achieves an average error reduction of 0.0626 and 0.0845 compared to the MK-MMD transfer learning method and self-attention ConvLSTM algorithms, respectively, and exhibits higher predictive reliability. This method can provide valuable insights for lifespan prediction challenges concerning bearings in ship propulsion shaft systems under various operational conditions, as well as similar cross-domain lifespan prediction problems.

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Section: Introductionmentioning

confidence: 99%