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Motor bearing fault diagnosis method based on artificial neural network

An artificial neural network and motor bearing technology, which is applied in the direction of mechanical bearing testing, mechanical component testing, machine/structural component testing, etc., can solve problems such as inability to accurately identify fault types, achieve strong anti-interference ability, and identify The effect of high accuracy and strong practicality

Inactive Publication Date: 2018-02-02
王才旺
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  • Abstract
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  • Application Information

AI Technical Summary

Problems solved by technology

At present, the diagnosis method of motor bearing faults is mainly the vibration method. The vibration method can effectively detect various faults of the motor and is easy to operate. However, the vibration method can only detect signals and cannot accurately identify the type of fault.

Method used

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  • Motor bearing fault diagnosis method based on artificial neural network
  • Motor bearing fault diagnosis method based on artificial neural network
  • Motor bearing fault diagnosis method based on artificial neural network

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Embodiment Construction

[0026] The technical solution of the present invention is clearly and completely described below, obviously, the described embodiments are only some embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts belong to the protection scope of the present invention.

[0027] The motor bearing fault diagnosis method based on artificial neural network of the present invention, comprises the following steps successively:

[0028] (1) Feature vector extraction: use the vibration sensor to collect the vibration signals of the inner ring, outer ring and rolling elements of the motor bearing in different states as feature data, form a feature vector, and use it as the input of the neural network;

[0029] Extract the four time-domain features of the inner ring, outer ring and rolling elements of the motor bearing, including mean value, va...

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Abstract

The invention discloses a motor bearing fault diagnosis method based on an artificial neural network. Firstly the vibration signals of the internal circle, the external circle and the rolling body ofa motor bearing under different states are acquired to act as feature data by using a vibration sensor to form feature vectors and act as the input of the neural network; then the number of nodes of the artificial neural network is determined according to the number of dimensions of the extracted feature vectors; the artificial neural network is designed and then a part of the feature signals actas training samples, and the expected output is obtained by using the trained neural network; and finally the residual feature signals act as verification samples to diagnose the fault point of the motor bearing. Various types of faults of the motor bearing can be judged by using the artificial neural network. The artificial neural network can be used for identifying the complex or unknown system.The artificial neural network has high anti-interference capability, and identification of the neural network is not influenced even the system is under interference. Besides, the artificial neural network has high identification accuracy and high practicality.

Description

technical field [0001] The invention relates to the field of motor bearing faults, in particular to an artificial neural network-based fault diagnosis method for motor bearings. Background technique [0002] In modern production, the motor plays an irreplaceable role. If the motor fails, it will stop production and even endanger human life. Rolling bearing is the most important part of the motor, and its normal operation has an important impact on the mechanical properties of the motor. At present, the diagnosis method of motor bearing faults is mainly the vibration method. The vibration method can effectively detect various faults of the motor and is easy to operate. However, the vibration method can only detect signals and cannot accurately identify the fault type. Contents of the invention [0003] The purpose of the present invention is to provide a motor bearing fault diagnosis method based on artificial neural network, which can accurately identify the fault type of...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G01M13/04
CPCG01M13/045
Inventor 王才旺
Owner 王才旺
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