Abstract
Since the traditional fault diagnosis method of the marine fuel system has a low accuracy of identification, the algorithm solution can easily fall into local optimum, and they are not fit for the research on the fault diagnosis of a marine fuel system. Hence, a fault diagnosis method for a marine fuel system based on the SaDE-ELM algorithm is proposed. First, the parameters of initializing extreme learning machine are adopted by a differential evolution algorithm. Second, the fault diagnosis of the marine fuel system is realized by the fault diagnosis model corresponding to the state training of marine fuel system. Based on the obtained fault data of a marine fuel system, the proposed method is verified. The experimental results show that this method produces higher recognition accuracy and faster recognition speed that are superior to the traditional BP neural network, SVM support vector machine diagnosis algorithm, and the un-optimized extreme learning machine algorithm. The results have important significance relevant to fault diagnosis for a marine fuel system. The algorithm based on SaDE-ELM is an effective and practical method of fault diagnosis for a marine fuel system.
Highlights
The marine fuel system is one of the important parts of a ship
Extract the feature vectors of the state of the marine fuel system and use Gaussian white noise to expand the data samples; A differential evolution algorithm is used to adaptively acquire the extreme learning initialization parameters, and the fault diagnosis model for marine fuel system is constructed for different types of data, and the minimum value of the objective function is estimated; Get the test model with SaDE-Extreme Learning Machine (ELM) training and diagnose the most probable state of the marine fuel system according to the new sample
In view of the low accuracy of the existing fault diagnosis methods for marine fuel systems, this paper proposes a fault diagnosis method for marine fuel system based on SaDE-ELM and extracts feature vector of eight dimensions depending on the characteristics of the marine fuel system
Summary
The marine fuel system is one of the important parts of a ship. A fuel injection system with a high-pressure pump, a high-pressure oil pipe, and an injector is an important part of the fuel machine. The state of each subsystem is diagnosed based on the SVM support vector machine algorithm, and an association rule mining algorithm is utilized to mine the implicit fault relationship between subsystems and realize the fault diagnosis of the marine diesel engine. Zhang et al [10] propose a genetic algorithm based on high-frequency demodulation analysis training support vector machine for diesel engine cylinder head fault diagnosis. Aiming at the feature of small collection of data of the fault model for marine fuel system, this research adopts white Gaussian noise to enlarge primary data, and the fault diagnosis method for marine fuel system based on a SaDE-ELM algorithm and fault data are used to construct a fault diagnosis model, which further finish the fault recognition and realize accurate recognition to different operative mode of marine fuel system. It can provide a novel idea and method for state detection and fault diagnosis of a ship fuel system
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