Micro-vibration motor defect failure classification method and device based on convolutional neural network

A technology of convolutional neural network and vibration motor, applied in the direction of biological neural network model, neural learning method, neural architecture, etc., can solve the problems of difficulty in ensuring accuracy and low efficiency, and achieve high-precision detection, high detection accuracy, and improved The effect of poor generalization ability

Active Publication Date: 2019-06-28
SICHUAN UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

[0006] Aiming at the problems of low efficiency and difficulty in ensuring the accuracy of defect detection of micro vibration motors based on artificial vision, the purpose of the present invention is to provide a method and device for classifying defects and faults of micro vibration motors based on convolutional neural network. While accurately classifying micro-vibration motor defects and faults, it simplifies operation difficulty and improves detection efficiency

Method used

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  • Micro-vibration motor defect failure classification method and device based on convolutional neural network
  • Micro-vibration motor defect failure classification method and device based on convolutional neural network
  • Micro-vibration motor defect failure classification method and device based on convolutional neural network

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Embodiment 1

[0044] The micro-vibration motor defect classification device provided in this embodiment, such as Figure 1 to Figure 3 As shown, it includes a micro-vibration motor fixture 1 , a micro-control unit 2 , a power supply 3 , a collection resistor 4 , a start-up resistor, a data collection card 5 and a computer 6 .

[0045] like image 3 As shown, the micro vibration motor fixture 1 is provided with a number of card slots 11 for installing the micro vibration motor 7, and two electrodes 12 corresponding to the electrical connection ports of the micro vibration motor are designed on one side of the slot wall of the card slot. It matches the appearance of the micro vibration motor; the eccentric block of the micro vibration motor extends from the groove wall on the other side of the slot, and the slot wall of the slot is designed with a limit structure to prevent the axial movement of the micro vibration motor. It is a limit piece fixed on the inner side wall of the card slot, and...

Embodiment 2

[0048] In this embodiment, the labview software is used to collect the voltage signal, and the OpenCV support package of python is used to process the collected voltage signal image. The computers all use serial communication, the host uses its own serial port, the labview software uses the visa serial communication support package, and the python uses the pyseries serial communication support package.

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Abstract

The invention discloses a micro-vibration motor defect failure classification method and a micro-vibration motor defect failure classification device based on a convolutional neural network. Voltage signal images acquired at the two ends of an acquisition resistance connected in series with a micro-vibration motor electrifying loop are input into a trained convolutional neural network model, accurate identification of the micro-vibration motor defect varieties can be realized, the whole process is automatic identification operation, staff does not need to take part in the whole generation process too much, the detection efficiency is greatly improved and the labor production cost is reduced.

Description

technical field [0001] The invention belongs to the technical field of machine defect detection, and relates to a deep learning-based micro-vibration motor defect detection technology, in particular to a convolutional neural network-based micro-vibration motor defect method classification and device. Background technique [0002] Micro vibration motors are widely used in electronic devices such as mobile phones and smart wearables. With the rapid development of interactive electronic equipment in my country, the demand for micro vibration motors is increasing day by day, and the annual demand has reached more than 2 billion. How to quickly detect defective products in the production line has become a bottleneck limiting motor output. [0003] The mechanical vibration caused by the bearing defect of the micro vibration motor will lead to the eccentric oscillation of the air gap width, which will cause the change of the magnetic flux density. The change of the magnetic flux d...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G01R31/34G06K9/00G06K9/32G06K9/38G06K9/62G06N3/04G06N3/08G06T5/30
Inventor 方夏黄思思刘剑歌王杰冯涛
Owner SICHUAN UNIV
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