Grey Relational Analysis-Based Fault Prediction for Watercraft Equipment

Feng, Shasha; Chen, Zijian; Guan, Qunsheng; Yue, Jingtao; Xia, Chengyi

doi:10.3389/fphy.2022.885768

Cited by 3 publications

(2 citation statements)

References 46 publications

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“…In severe cases, engine shutdowns can occur, damaging associated equipment, disrupting the normal operation of the vessel, and posing a risk to the safety of personnel and property on board [5]. Due to prolonged exposure to harsh operating environments, MDE components experience severe wear, significantly increasing the risk of potential malfunctions and failures [6], However, traditional MDE condition monitoring techniques typically focus on monitoring the thermodynamic parameters of the engine, such as gas pressure and oil temperature. These parameters only show significant changes when the malfunction has reached a certain severity [7].…”

Section: Introductionmentioning

confidence: 99%

Short-Term Exhaust Gas Temperature Trend Prediction of a Marine Diesel Engine Based on an Improved Slime Mold Algorithm-Optimized Bidirectional Long Short-Term Memory—Temporal Pattern Attention Ensemble Model

Sun,

Zeng,

2024

JMSE

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As the core component of a ship’s engine room, the operation of a marine diesel engine (MDE) directly affects the economy and safety of the entire vessel. Predicting the future changes in the status parameters of a MDE helps to understand the operational status, enabling timely warnings to the engine crew, and to ensure the safe navigation of the vessel. Therefore, this paper combines the temporal pattern attention mechanism with the bidirectional long short-term memory (BiLSTM) network to propose a novel trend prediction method for short-term exhaust gas temperature (EGT) forecasting. First, the Pearson correlation analysis (PCA) is conducted to identify input feature variables that are strongly correlated with the EGT. Next, the BiLSTM network models input feature variables such as load, fuel oil pressure, and scavenging air pressure and capture the interrelationships between different vectors from the hidden layer matrix within the BiLSTM network. This allows the selection of valuable information across different time steps. Meanwhile, the temporal pattern attention (TPA) mechanism has the ability to explore complex nonlinear dependencies between different time steps and series. This assigns appropriate weights to the feature variables within different time steps of the BiLSTM hidden layer, thereby influencing the input effect. Finally, the improved slime mold algorithm (ISMA) is utilized to optimize the hyperparameters of the prediction model to achieve the best level of short-term EGT trend prediction performance based on the ISMA-BiLSTM-TPA model. The prediction results show that the mean square error, the mean absolute percentage error, the root mean square error and the coefficient of determination of the model are 0.4284, 0.1076, 0.6545 and 98.2%, respectively. These values are significantly better than those of other prediction methods, thus fully validating the stability and accuracy of the model proposed in this paper.

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Section: Introductionmentioning

confidence: 99%

Short-Term Exhaust Gas Temperature Trend Prediction of a Marine Diesel Engine Based on an Improved Slime Mold Algorithm-Optimized Bidirectional Long Short-Term Memory—Temporal Pattern Attention Ensemble Model

Sun,

Zeng,

2024

JMSE

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“…Therefore, using the grey relational analysis method for multi-attribute evaluation of risk control measures is a more suitable approach. The grey system theory was first proposed by Deng in 1989 [19] and, since then, it has been an important topic of discussion in multiple-attribute decision-making applications in engineering, e.g., in Feng et al [20], Grdinic-Rakonjac et al [21], Dey et al [22], Ramesh et al [23], and Zhou et al [24]. In an FSA study of a ship, Li and Tang [25] initially ranked the RCOs by using a GRD-based multi-attribute assessment.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Multi-Attribute Decision-Making Method for Risk-Based Ship Design

Li,

Zhang,

Yuan

2024

Applied Sciences

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Formal safety assessment (FSA) is regarded as an effective approach to support decision-making in shipbuilding to balance safety, technology, and cost. However, the selection of risk control options (RCOs) in the FSA process still needs to be studied before the FSA becomes a generic approach. This study proposed a multi-attribute-based assessing model to support the decision-making process regarding RCOs. The attributes of RCOs were divided into the performance and cost-effectiveness attribute sets. Moreover, a dynamic selection procedure of attributes was designed based on the ‘as low as reasonable and practicable’ (ALARP) principle. The application of the dynamic multi-attribute model can make it possible to rank RCOs by considering the changes in the decision-makers’ risk aversion to risk levels. In this model, a comprehensive weighting method based on game theory was used to balance the subjective and objective weights of the attributes. An improved grey rational analysis (GRA) was used to perform the multi-attribute assessment of RCOs. Therefore, this dynamic multi-attribute model is combined with the ALARP principle and evaluated using GRA. Finally, a case regarding crude tankers was studied using the proposed model to verify the feasibility and reliability of the dynamic multi-attribute model.

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Research on Fault Early Warning of Marine Diesel Engine Based on CNN-BiGRU

Liu

Gan

Chen

et al. 2022

JMSE

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The normal operation of the marine diesel engine is of great significance to ensure the normal navigation of the ship. Predicting its operation state and judging whether the diesel engine is in the abnormal state in advance can guarantee the safe navigation of the vessel. In this paper, combining the feature extraction ability of the convolutional neural network (CNN) and the time series data prediction ability of the bidirectional gated recurrent unit (BiGRU), a marine diesel engine exhaust temperature prediction model is constructed. The results show that the mean square error (MSE) of the prediction model is 0.1156, the average absolute error (MAE) is 0.2501, and the average absolute percentage error (MAPE) is 0.0005336. Then, according to the residual distribution between the predicted value and the actual value of the model output and the standard deviation of the residual calculated by using the sliding window, we set the alarm threshold, where the upper limit of residual error is 1 and the lower limit is 1. The upper limit of the standard deviation is 0.604. Finally, we used the data set under abnormal conditions for experimental verification. The results show that the method can accurately determine the fault early warning of the marine diesel engine and provides a new reference for the health management of intelligent marine equipment.

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Grey Relational Analysis-Based Fault Prediction for Watercraft Equipment

Cited by 3 publications

References 46 publications

Short-Term Exhaust Gas Temperature Trend Prediction of a Marine Diesel Engine Based on an Improved Slime Mold Algorithm-Optimized Bidirectional Long Short-Term Memory—Temporal Pattern Attention Ensemble Model

Short-Term Exhaust Gas Temperature Trend Prediction of a Marine Diesel Engine Based on an Improved Slime Mold Algorithm-Optimized Bidirectional Long Short-Term Memory—Temporal Pattern Attention Ensemble Model

Dynamic Multi-Attribute Decision-Making Method for Risk-Based Ship Design

Research on Fault Early Warning of Marine Diesel Engine Based on CNN-BiGRU

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