Ship electric propulsion system fault diagnosis method based on ABC-SVM expert system

An electric propulsion and system fault technology, applied in the field of ship fault diagnosis, can solve the problems of poor stability and high error rate of fault diagnosis results, achieve good generalization ability, improve accuracy, and improve practicability and real-time performance.

Inactive Publication Date: 2020-12-15
HARBIN ENG UNIV
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Problems solved by technology

Therefore, the expert system based on the neural network has relatively poor stability in the fault diagnosis results of the ship electric propulsion system, and sometimes there will be a fault diagnosis phenomenon with a high error rate.

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  • Ship electric propulsion system fault diagnosis method based on ABC-SVM expert system
  • Ship electric propulsion system fault diagnosis method based on ABC-SVM expert system
  • Ship electric propulsion system fault diagnosis method based on ABC-SVM expert system

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Embodiment Construction

[0043] The present invention is described in more detail below in conjunction with accompanying drawing example:

[0044] combine Figure 1-3, a kind of ship electric propulsion system fault diagnosis method based on the expert system of ABC-SVM of the present invention, mainly takes expert system as model, uses the support vector machine that uses artificial bee colony optimization in the reasoning machine of expert system, to ship electric power Diagnose faults in the propulsion system. The core of the expert system is the inference engine and the knowledge base. The knowledge and experience of experts are stored in the knowledge base, and then the support vector machine using artificial bee colony optimization is used in the inference engine. After the knowledge in the knowledge base is preprocessed, It is used to classify and train the inference engine, and save the trained model; store the real-time data collected from the ship electric propulsion system in the dynamic d...

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Abstract

The invention aims to provide a ship electric propulsion system fault diagnosis method based on an ABC-SVM expert system, knowledge and experience of experts are stored in a knowledge base, a supportvector machine optimized by using an artificial bee colony is used in an inference machine, the knowledge in the knowledge base is preprocessed and then is used for performing classification trainingon the inference machine and storing a trained model. real-time data collected from the ship electric propulsion system is stored in a dynamic database, feature value extraction is conducted, the datais input into a trained inference engine, whether the ship electric propulsion system breaks down or not is judged, which device breaks down is judged, and the reason why the fault occurs is explained. Practicability and real-time performance of an expert system are greatly improved, the problem of a local optimal solution of the support vector machine is solved to a great extent, and the accuracy of fault diagnosis is improved.

Description

technical field [0001] The invention relates to a fault diagnosis method, in particular to a ship fault diagnosis method. Background technique [0002] At present, the ship structure is becoming more and more complex, the tonnage of the ship is also increasing, and the power to be provided is also increasing. As the main provider of ship power, the ship electric propulsion system has a very important position. In practical applications, the ship electric propulsion system is affected by various factors and its own aging, and the probability of failure of the ship electric propulsion system is increasing day by day. The failure of the current ship electric propulsion system will bring adverse effects on the normal operation and navigation of the ship, and the relationship between the various components of the ship electric propulsion system is very complicated, which makes the fault diagnosis of the ship electric propulsion system face a huge challenge. [0003] In fault dia...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F30/27G06K9/62G06N3/00G06N5/04G06F111/06
CPCG06F30/27G06N3/006G06N5/04G06F2111/06G06F18/2411
Inventor 丁宇马生平贲虹凯随从标
Owner HARBIN ENG UNIV
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