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Improved empirical mode decomposition method for fault diagnosis of ship electric propulsion system

A technology of empirical mode decomposition and electric propulsion, applied in biological neural network models, neural architecture, character and pattern recognition, etc., can solve the problems of reduced effect of intrinsic mode function decomposition, loss of meaning of EMD decomposition, loss of physical meaning, etc. , to achieve the effect of clear physical meaning, clear meaning of feature extraction, and elimination of modal aliasing

Pending Publication Date: 2019-01-01
SHANGHAI MARITIME UNIVERSITY
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AI Technical Summary

Problems solved by technology

In the decomposition process, once the modal aliasing occurs, it will accumulate and affect the subsequent decomposition components, resulting in a gradual decrease in the decomposition effect of the intrinsic mode function, thus losing the proper physical meaning, and even losing the meaning of EMD decomposition

Method used

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  • Improved empirical mode decomposition method for fault diagnosis of ship electric propulsion system
  • Improved empirical mode decomposition method for fault diagnosis of ship electric propulsion system
  • Improved empirical mode decomposition method for fault diagnosis of ship electric propulsion system

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Embodiment 1

[0062] Such as figure 2 As shown, a fault diagnosis method for ship electric propulsion system based on improved empirical mode decomposition includes the following steps:

[0063] 1. Obtain the failure data of the ship's electric propulsion system;

[0064] 2. Obtain the intrinsic mode function data by improving the empirical mode decomposition of the fault data;

[0065] 3. Obtain the intrinsic mode function data for different parts and conduct RBF neural network analysis to obtain the cause of the failure.

[0066] For the terminal flying wing problem, the commonly used improved algorithms are the neural network method and the continuation method, and there are still some problems in these methods. This application proposes an improved algorithm for end-flying wings combining genetic algorithm and cosine window.

[0067] Because the cosine window function replaces the rectangular function that intercepts signal samples with a relatively smooth window function, and perfo...

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Abstract

The invention discloses an improved empirical mode decomposition method for fault diagnosis of ship electric propulsion system, which comprises the following steps: obtaining fault data of ship electric propulsion system; improved empirical mode decomposition (EMD) being used to obtain the intrinsic mode function (IMF) data from the fault data; RBF neural network being used to analyze the intrinsic mode function data of different parts and the fault reason is obtained. The improved Empirical Mode Decomposition (EMD) process is as follows: signal input; determining an initialization parameter Tby using the cosine window definition; the original data are extended by genetic algorithm. Windowing the data to improve the endpoint effect; Empirical Mode Decomposition for Eliminating Modal Aliasing of Data; The intrinsic mode function data are intercepted. An improved empirical mode decomposition (EMD) method for fault diagnosis of ship electric propulsion system is disclosed, which is suitable for non-stationary, non-linear and multi-component signal characteristics of ship propulsion system fault, so as to improve fault signal analysis capability.

Description

technical field [0001] The invention relates to an improved empirical mode decomposition method for fault diagnosis of a ship's electric propulsion system. Background technique [0002] With the vigorous development of the maritime industry, electric propulsion systems are widely used on ships. The electric propulsion system is the only source of power for modern ships sailing in isolated seas, and is known as the lifeline of ships. Due to the complexity of the electric propulsion system and the harshness of its operating sea conditions, the ship's electric propulsion system exhibits instability and exceeding The normal dynamic range causes unpredictable ship failures. Therefore, in order to improve the safety of ship operation, reduce unnecessary economic losses, and ensure the safety of crew members, the fault diagnosis of ship electric propulsion systems has attracted the attention and rapid development of scholars. [0003] Empirical Mode Decomposition (EMD) was first ...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/00G06N3/04
CPCG06N3/045G06F2218/00Y02T90/00
Inventor 胡红钱施伟锋卓金宝谢嘉令
Owner SHANGHAI MARITIME UNIVERSITY
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