Gear failure diagnosis method based on adaptive genetic algorithm and SOM (Self-Organizing Map) network

A genetic algorithm and fault diagnosis technology, applied in machine gear/transmission mechanism testing, calculation, computer parts, etc., can solve problems such as poor convergence, poor reliability, and limited reliability, and achieve the effect of improving accuracy and reliability

Inactive Publication Date: 2016-02-17
HENAN POLYTECHNIC UNIV
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Problems solved by technology

The neural network has a simple structure and strong problem-solving ability, and can handle noisy data well, but the algorithm has local optimal problems, poor convergence, and limited reliability
[0003] It can be seen that in the prior art, the gear fault diagnosis method has problems such as low precision and poor reliability.

Method used

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  • Gear failure diagnosis method based on adaptive genetic algorithm and SOM (Self-Organizing Map) network
  • Gear failure diagnosis method based on adaptive genetic algorithm and SOM (Self-Organizing Map) network
  • Gear failure diagnosis method based on adaptive genetic algorithm and SOM (Self-Organizing Map) network

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Experimental program
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Embodiment

[0086] The acceleration sensor is used to collect the vibration signal data of the gears in the four states of gearbox transmission system normal, tooth surface wear, tooth surface scratches and broken teeth. The collected 300 sets of gear type data are divided into 200 sets of training data and 100 sets of testing Data, use the trained SOM network to diagnose the test data, Figure 4 Flow chart for SOM network fault diagnosis. Part of the training data of the SOM network is shown in Table 1. The diagnosis result of SOM network is shown in Table 2.

[0087] Table 1 Part of the training data of the SOM network

[0088]

[0089] Table 2 Diagnosis results of SOM network

[0090]

[0091]

[0092] According to the data in Table 2, when the number of training steps is 10, the training data 1 and 2 are divided into one category, and the 3, 4, 5, 6, 7, and 8 are divided into another category. The SOM network performs preliminary data Classification, when the number of training steps is 1...

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Abstract

The invention provides a gear failure diagnosis method based on an adaptive genetic algorithm and an SOM (Self-Organizing Map) network. The method comprises the following steps: obtaining a vibration signal of a gear, carrying out wavelet packet analysis on the vibration signal, extracting the feature vectors of the vibration signal, dividing the feature vectors into training data and test data, firstly, utilizing the training data to train the SOM network optimized by the adaptive genetic algorithm, continuously updating the weight and the threshold value of the SOM network until an error output by the SOM network meets an accuracy requirement or achieves a maximum iteration, then, adopting the trained SOM network to diagnose a fault type of the test data, and outputting a fault diagnosis result of the gear. The gear failure diagnosis method has the characteristics of being high in precision, high in reliability and the like, and can be widely applied to the field of the fault diagnosis of mechanical equipment.

Description

Technical field [0001] The invention relates to the technical field of fault diagnosis, in particular to a gear fault diagnosis method based on an adaptive genetic algorithm and a SOM network. Background technique [0002] As an indispensable power transmission component in the transmission system of mechanical equipment, gears directly affect the working efficiency, reliability and life of the entire mechanical equipment. For the fault diagnosis of gears, scholars at home and abroad have proposed a variety of fault diagnosis methods. Mainly use rough set theory, support vector machine, Bayesian classification, fuzzy logic, neural network and other methods to diagnose gear faults. Rough set theory has great advantages in processing fuzzy and uncertain information, but its decision-making rules are very unstable, with poor accuracy, and it is based on a complete information system. When processing data, data loss is often encountered. Support vector machines have advantages in s...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/00G01M13/02
CPCG01M13/021G01M13/028G06F2218/12G06F2218/08
Inventor 刘景艳张伟郭顺京王晓卫
Owner HENAN POLYTECHNIC UNIV
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