Fault diagnosis of ship power equipment based on adaptive neural network
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Dongfang Zhang
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.
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Author contributions: The author has accepted responsibility for the entire content of this submitted manuscript and approved submission.
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Research funding: The author(s) received no specific funding for this study.
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Conflict of interest statement: The author declares that they have no conflicts of interest to report regarding the present study.
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Data availability statement: The data underlying the results presented in the study are available within the manuscript.
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Articles in the same Issue
- Frontmatter
- Research Articles
- Fault diagnosis of ship power equipment based on adaptive neural network
- Application of sustainable power and laser washing device in garment design
- Automatic monitoring system of power equipment based on Internet of Things technology
- Intelligent algorithm of electrical fire monitoring system based on data mining technology
- Vector correlation learning and pairwise optimization feature selection for false data injection attack detection in smart grid
- ZIP load modeling for single and aggregate loads and CVR factor estimation
- Upper limb movement simulation and biomechanical characteristics during human movement
- Intelligent home control system based on BP neural network speech recognition
- User-side precision marketing model of integrated energy service system
- Building carbon neutrality goals break down strategies for sustainable energy development
- Energy-saving intelligent manufacturing optimization scheme for new energy vehicles
