Residual fatigue life prediction for ship propeller based on test signal characteristic fusion and TV-HSMM: An experimental case study

Jinlong, Wang; Sibo, Gao; Shujie, Liu; Xiukun, Ji; Zeyu, Shi

doi:10.1016/j.oceaneng.2024.116944

Cited by 1 publication

(1 citation statement)

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“…As the maritime industry adapts to environmental regulations, the fatigue resistance of ships' mechanical components, such as propellers and engines, is becoming increasingly critical. The implementation of ESDs, as well as efficient operations such as slow steaming, is pivotal for reducing emissions, although it necessitates a thorough understanding of the fatigue behavior of these components under operational loads [5]. While it may seem easy, identifying inefficient operations and identifying anomalies requires a set of data, including normal and abnormal conditions.…”

Section: Introductionmentioning

confidence: 99%

A Prescriptive Model for Failure Analysis in Ship Machinery Monitoring Using Generative Adversarial Networks

Yigin,

Celik

2024

JMSE

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In recent years, advanced methods and smart solutions have been investigated for the safe, secure, and environmentally friendly operation of ships. Since data acquisition capabilities have improved, data processing has become of great importance for ship operators. In this study, we introduce a novel approach to ship machinery monitoring, employing generative adversarial networks (GANs) augmented with failure mode and effect analysis (FMEA), to address a spectrum of failure modes in diesel generators. GANs are emerging unsupervised deep learning models known for their ability to generate realistic samples that are used to amplify a number of failures within training datasets. Our model specifically targets critical failure modes, such as mechanical wear and tear on turbochargers and fuel injection system failures, which can have environmental effects, providing a comprehensive framework for anomaly detection. By integrating FMEA into our GAN model, we do not stop at detecting these failures; we also enable timely interventions and improvements in operational efficiency in the maritime industry. This methodology not only boosts the reliability of diesel generators, but also sets a precedent for prescriptive maintenance approaches in the maritime industry. The model was demonstrated with real-time data, including 33 features, gathered from a diesel generator installed on a 310,000 DWT oil tanker. The developed algorithm provides high-accuracy results, achieving 83.13% accuracy. The final model demonstrates a precision score of 36.91%, a recall score of 83.47%, and an F1 score of 51.18%. The model strikes a balance between precision and recall in order to eliminate operational drift and enables potential early action in identified positive cases. This study contributes to managing operational excellence in tanker ship fleets. Furthermore, this study could be expanded to enhance the current functionalities of engine health management software products.

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Section: Introductionmentioning

confidence: 99%