Ship Fault Research Articles

Ship Ballast Water System Fault Diagnosis Method Based on Multi-Feature Fusion Graph Convolution

To tackle the issues of limited fault data, inadequate information availability, and subpar fault diagnosis within the realm of ship ballast water system condition monitoring, this paper presents a novel fault diagnosis methodology known as the Probabilistic Similarity and Linear Similarity-based Graph Convolutional Neural Network (PCGCN) model. PCGCN initially converts the ship’s ballast water system dataset into two distinct graph structures: a probabilistic topology graph and a correlation topology graph. It delves into data similarity by employing T-SNE for probabilistic similarity and Pearson’s correlation coefficient for linear similarity to establish the inter-sample neighbor relationships. Subsequently, an early fusion of these two graph structures is conducted to extract more profound multi-scale feature information. Following this step, the graph convolutional neural network (GCN) is introduced to amalgamate the feature information from neighboring nodes in addition to its inherent features. This is aimed at enhancing the available information for the classification task and addressing the issues of limited fault data and inadequate label information. In conclusion, we employ a simulated ship fault dataset for testing experiments, and the PCGCN model demonstrates superior classification accuracy, reaching 97.49%, outperforming traditional diagnostic methods. These experimental outcomes underscore the applicability of the model introduced in this study to the realm of ship ballast water system fault diagnosis, even under challenging conditions characterized by limited sample sizes and insufficient labeling information.

Intelligent Fault Diagnosis of Variable-Condition Motors Using a Dual-Mode Fusion Attention Residual

Electric motors play a crucial role in ship systems. Detecting potential issues with electric motors is a critical aspect of ship fault diagnosis. Fault diagnosis in motors is often challenging due to limited and noisy vibration signals. Existing deep learning methods struggle to extract the underlying correlation between samples while being susceptible to noise interference during the feature extraction process. To overcome these issues, this study proposes an intelligent bimodal fusion attention residual model. Firstly, the vibration signal to be encoded undergoes demodulation and is divided into high and low frequencies using the IEEMD (Improved Ensemble Empirical Mode Decomposition) composed of the EEMD (Ensemble Empirical Mode Decomposition) and the MASM (the Mean of the Standardized Accumulated Modes). Subsequently, the high-frequency component is effectively denoised using the wavelet packet threshold method. Secondly, current data and vibration signals are transformed into two-dimensional images using the Gramian Angular Summation Field (GASF) and aggregated into a bimodal Gramian Angle Field diagram. Finally, the proposed model incorporates the Self-Attention Squeeze-and-Excitation Networks (SE) mechanism with the Swish activation function and utilizes the ResNeXt architecture with a Dropout layer to identify and diagnose faults in the multi-mode fusion dataset of motors under various working conditions. Based on the experimental results, a comprehensive discussion and analysis were conducted to evaluate the performance of the proposed intelligent bimodal fusion attention residual model. The results demonstrated that, in comparison to traditional methods and other deep learning models, the proposed model effectively utilized multimodal data, thereby enhancing the accuracy and robustness of fault diagnosis. The introduction of attention mechanisms and residual learning enable the model to focus more effectively on crucial modal data and learn the correlations between modalities, thus improving the overall performance of fault diagnosis.

Ranking method of equipment failure risk in shipboard power system

The realization of equipment failure risk ranking in shipboard power systems is of positive significance to the rapid recovery of ship power system faults. To effectively improve the speed and accuracy of ship fault recovery, accident plans are developed according to ranking results for the top n equipment with high risk. A risk ranking method for distribution equipment is proposed, which takes the outage power, failure probability, MTTR (mean time to repair), and equipment priority of distribution equipment as evaluation indexes, and uses the ENTROPY-TOPSIS analysis method. Taking the DDG-1000 destroyer as an example, the ranking of the failure risk of shipboard power distribution equipment is realized. The calculated results are consistent with the theoretical results, which proves the correctness of the proposed method.

Fault diagnosis of ship power equipment based on adaptive neural network

Abstract In recent decades, international shipping trade has been developing continuously. The ship is the main transportation carrier of international shipping, and the power equipment on the ship is in an absolutely critical position, and its working condition is also directly related to the safe running of the ship. Therefore, the research on the ship’s power plant and fault diagnosis system is particularly important. Due to the actual operation of the ship power plant, the characteristics of the components are inevitably changed. Therefore, the corresponding equipment fault diagnosis technology also has a certain importance for the health management system of power equipment. As for the problem that the current ship equipment fault identification method is not widely applicable and the accuracy is not high enough, this paper aims to make the ship fault diagnosis faster and more accurately. It effectively solves the problem of timeliness and accuracy of fault diagnosis. This paper takes ship power equipment as the research object, firstly, introduces and proposes a diagnosis method of adaptive neural network structure, and applies it to fault detection and estimation. Next, this paper uses the adaptive neural network model for ship fault diagnosis. The accuracy of the adaptive neural network designed in this paper is better than that of the conventional neural network, and when the number of training samples is small. It can still obtain an ideal network through training to ensure that the fault detection of power equipment health parameters has high accuracy. The simulation results show that, compared with the common methods, the network model can effectively reduce the fault diagnosis error. The correct rate of fault diagnosis is over 93%, which improves the speed, accuracy, and applicability of fault diagnosis.

Intelligent Retrieval System of Ship Fault Information Based on Big Data Analysis

Intelligent Retrieval System of Ship Fault Information Based on Big Data Analysis

Research on Ship Fault Diagnosis Based on Deep Learning

This paper proposes a deep learning algorithm to diagnose ship faults in order to improve the accuracy and diagnostic efficiency of ship fault diagnosis. 90% of the large number of unlabeled ship operational data samples are selected for model training, and the remaining 10% is used for model testing. We optimize model parameters and improve the accuracy of the deep learning model for fault diagnosis classification. Hidden layer functions are used to extract multi-layer data features and perform feature fusion. Gain values are used to define ship faults, primary faults, secondary faults, and tertiary faults. Finally, we use the soft-max classifier to fault output the fault and get the fault level output. The experimental results on the ship simulation fault dataset show that compared with other traditional algorithms, the accuracy of the fault diagnosis of this method is greatly improved, and the simulation result is 92.5%. Experiments show that the model based on deep learning algorithm for multi-layer feature fusion training can better meet the needs of ship fault diagnosis under complex systems.

Large-scale Ship Fault Data Retrieval Algorithm Supporting Complex Query in Cloud Computing

ABSTRACT Zhang, S., 2019. Large-scale ship fault data retrieval algorithm supporting complex query in cloud computing. In: Hoang, A.T. and Aqeel Ashraf, M. (eds.), Research, Monitoring, and Enginee...

Massive Ship Fault Data Retrieval Algorithm Supporting Complex Query in Cloud Computing

Lou, H., 2019. Massive ship fault data retrieval algorithm supporting complex query in cloud computing. In: Guido-Aldana, P.A. and Mulahasan, S. (eds.), Advances in Water Resources and Exploration. Journal of Coastal Research, Special Issue No. 93, pp. 1013–1018. Coconut Creek (Florida), ISSN 0749-0208.Aiming at the problems of the current retrieval algorithm for massive ship fault data retrieval in cloud computing, which does not support complex query, large retrieval cost and low precision, a massive ship fault data retrieval algorithm supporting complex query in cloud computing based on query performance prediction is proposed. Introducing the Lagrangian algorithm to preprocess the ship fault data information; combining the attribute values of the ship fault data and the context to calculate the similarity between different attributes, and identifying the same attribute according to the similarity; The attribute recognition result is queried by using multiple retrieval models, and the model complex query request is converted into a one-dimensional query key value, and the query key values obtained by the plurality of models are predicted, and the search result with the optimal prediction performance of the complex query mode is used as the final result. The experimental results show that the proposed algorithm has good retrieval accuracy in complex query mode, and it has less overhead than the current search algorithm.

船用劣质燃油对柴油机危害性分析及对策

随着国际航运的不断发展，规模壮大，加上国际油价的上涨以及原油资源的逐渐枯竭，使得炼油厂提高了原油提炼技术，使得重油品质的下降，导致船舶发动机故障率提高，严重的影响了船舶的正常运行。根据数据统计，重油品质每况愈下，随之船舶故障频率增高，这严重影响了航运市场的发展前景。本论文主要是通过对燃用重油的发动机进行探讨，针对重油的炼制，重油在船舶上的储运以及管理进行重点的探讨，还针对因重油带给发动机的危害进行了分析，以及提出处理危害的措施。 With the continuous development and growing size of international shipping, coupled with rising international oil prices and the gradual depletion of crude oil resources, the refinery improved crude oil refining technology, so that the quality of heavy oil decreased, resulting in ship engine failure rate, severely affected normal operation of the vessel. According to statistics, the deteriorating quality of heavy oil, followed by the ship fault frequency increased, which seriously affected the development prospects of the shipping market. This paper mainly through the HFO engine to explore, the focus will be on exploring heavy oil refining, storage and transportation of heavy oil on board the ship as well as management, but also analyzed the hazards brought to the engine because of heavy oil, and presented measures to process hazards.

An underwater ship fault detection method based on Sonar image processing

For the research of underwater ship fault detection method in conditions of sailing on the ocean especially in poor visibility muddy sea, a fault detection method under the assist of sonar image processing was proposed. Firstly, did sonar image denoising using the algorithm of pulse coupled neural network (PCNN); secondly, edge feature extraction for the image after denoising was carried out by morphological wavelet transform; Finally, interested regions Using relevant tracking method were taken, namely fault area mapping. The simulation results presented here proved the feasibility and effectiveness of the sonar image processing in underwater fault detection system.

A GAIN-SCHEDULING AND INTELLIGENCE FUSION METHOD FOR FAULT-TOLERANT CONTROL

In order to solve the problem on variable faults with time, this paper presents an intelligence fusion system based on gain scheduling control and hybrid neural networks for system fault detection and fault-tolerant control. In the system, an intelligent method based on hybrid neural networks is designed for state estimation and fault diagnosis, and a gain scheduling controller is adapted for system control. The system fusion scheme and its applications in ocean ship fault detection and fault tolerant control will be discussed.

An intelligence fusion system for ship fault-tolerant control

This paper presents an intelligence fusion system based on hybrid neural networks for system fault detection and fault-tolerant control. The design of system scheme, intelligent fusion methods, and its applications in ocean ship fault detection and fault tolerant control will be discussed.