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Fan gear box fault diagnosis method based on artificial intelligence algorithm

A technology of fault diagnosis and artificial intelligence, applied in the direction of machine gear/transmission mechanism testing, etc., can solve the problems of slow training speed, over-learning, and difficulty in guaranteeing fault diagnosis performance, etc., and achieve the effect of good reliability and high recognition rate

Inactive Publication Date: 2014-05-21
NORTH CHINA ELECTRIC POWER UNIV (BAODING)
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AI Technical Summary

Problems solved by technology

However, the use of neural networks for fault diagnosis has problems such as slow training speed and local optimal solution problems, and neural networks are suitable for large samples. When the number of samples is small, it is easy to cause over-learning.
Under normal circumstances, there are only a few dozen groups of sample data for fan gearbox failures, so it is difficult to guarantee the performance of fault diagnosis using neural networks

Method used

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  • Fan gear box fault diagnosis method based on artificial intelligence algorithm
  • Fan gear box fault diagnosis method based on artificial intelligence algorithm
  • Fan gear box fault diagnosis method based on artificial intelligence algorithm

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Embodiment Construction

[0043] The present invention will be further described below in conjunction with accompanying drawing and specific embodiment:

[0044] 1. Basic principle of artificial bee colony algorithm

[0045] The artificial bee colony algorithm (Artificial Bee Colony Algorithm, ABC algorithm) imitates the process of bees collecting honey in nature, including three types of bees: collecting bees, observing bees and scouting bees. A food source corresponds to a honey bee, that is to say, the number of food sources is equal to the number of honey bees. The positions of the food sources represent solutions to the optimization problem, and the quality or fitness of each solution corresponds to the amount of nectar in the food source.

[0046] First, initialize a file containing S N population of solutions. Each solution x i (i=1,2,...,S N ) is a D-dimensional column vector, and D is the number of optimization parameters. The bees search for new food sources in the neighborhood and comp...

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Abstract

The invention discloses a fan gear box fault diagnosis method based on an artificial intelligence algorithm. According to the method, structure features and fault types of a fan gear box are studied, and parameter optimization is performed on least square support vector machine (LSSVM) by an artificial bee colony algorithm to be applied to fault diagnosis of the fan gear box. By means of the method, the artificial bee colony algorithm is used for optimizing the LSSVM to excellently finish the fault diagnosis of the fan gear box, the recognition rate is high, and the reliability is good.

Description

Technical field: [0001] The invention relates to a fault diagnosis method for a fan gearbox based on an artificial intelligence algorithm. Background technique: [0002] With the continuous maturity of technology, the cost of wind power generation has gradually decreased, and its economic benefits have been continuously improved. At present, it is quite close to the cost of coal power. No matter in terms of traffic safety, environmental pollution and energy crisis, wind power has advantages over coal power. [0003] With the development of wind power technology and the increase in the number of wind turbines, a new industry has emerged, namely fault diagnosis and analysis. The fault diagnosis system can improve the benefits of wind power, and has very important practical significance for improving system security and reducing economic losses (Zhang Zhen. Research on gearbox fault diagnosis based on wavelet neural network expert system [D]. Yanshan University, 2010). [000...

Claims

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Application Information

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IPC IPC(8): G01M13/02
Inventor 赵文清蔡蕊
Owner NORTH CHINA ELECTRIC POWER UNIV (BAODING)
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