Method and device for fault classification of micro vibration motor defects based on convolutional neural network
A technology of convolutional neural network and vibration motor, which is 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 high The effect of detection accuracy
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Embodiment 1
[0044] The miniature vibrating motor fault classification device provided in this embodiment, such as Figure 1 to Figure 3 As shown, it includes a miniature vibration motor fixture 1, a micro control unit 2, a power supply 3, an acquisition resistor 4, a start resistor, a data acquisition card 5 and a computer 6.
[0045] Such as image 3 As shown, the micro-vibration motor fixture 1 is provided with a plurality of slots 11 for installing the micro-vibration motor 7, and two electrodes 12 corresponding to the electrical ports of the micro-vibration motor are designed on the slot wall on one side of the slot. It matches the appearance of the micro vibration motor; the eccentric block of the micro vibration motor extends from the wall on the other side of the card slot, and the wall of the card slot is designed with a limit structure to prevent the micro vibration motor from moving axially. It is a limiting piece fixed on the inner wall of the card slot, and a limiting opening...
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. All computers use serial port communication, the host uses its own serial port, the labview software uses visa serial port communication support package, and python uses pyseries serial port communication support package.
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