Deep learning-based asynchronous motor fault diagnosis method

A technology for fault diagnosis of asynchronous motors, applied in the computer field, can solve problems such as unstable vibration signals, complex motor structure, and difficult fault diagnosis of asynchronous motors, and achieve effective diagnosis

Pending Publication Date: 2019-07-30
HARBIN UNIV OF SCI & TECH
View PDF0 Cites 4 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

At present, for the common fault types of asynchronous motors, traditional fault diagnosis methods have been able to effectively solve them, but problems such as the difficulty i

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Deep learning-based asynchronous motor fault diagnosis method
  • Deep learning-based asynchronous motor fault diagnosis method
  • Deep learning-based asynchronous motor fault diagnosis method

Examples

Experimental program
Comparison scheme
Effect test

Example Embodiment

[0024] Example 1: Please refer to Figure 1-2 , A method for fault diagnosis of asynchronous motors based on deep learning, including the following steps:

[0025] A. Build a simulation experiment platform for asynchronous motor operation; the simulation experiment platform is mainly composed of a computer, load controller, asynchronous motor, tachometer, current sensor, acceleration sensor, NI data acquisition card, etc. The faults that the plan can diagnose include rotor imbalance, stator winding fault, stator winding broken turns, bearing fault, rotor bending, and rotor broken bars. In order to ensure the diversity of experimental data, a variety of different working conditions will be simulated by changing the speed and load conditions of the asynchronous motor.

[0026] B. Collect the current signal and vibration signal of the simulated motor fault state; the input sample to be collected should contain all the characteristics of the fault signal as much as possible. Since the ...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

PUM

No PUM Login to view more

Abstract

The invention discloses a deep learning-based asynchronous motor fault diagnosis method. The method comprises the following steps of A, building a simulation experiment platform for asynchronous motoroperation; B, carrying out current signal and vibration signal acquisition on a simulated motor fault state; C, extracting internal features of acquired data and carrying out corresponding labellingto complete the construction of a data set; D, building a stack type encoder-based fault diagnosis model and training a network and a classifier in sequence; and E, training a built deep neural network by utilizing the constructed data set, and verifying the fault diagnosis method by combining the simulation experiment platform. According to the method, a deep learning theory is imported to designa system which is capable of correctly, sensitively and effectively diagnosing complicated faults of asynchronous motors, so that the method can solve the problems existing in the fault diagnosis ofthe asynchronous motors and adapt to the requirements of constantly developing electric systems.

Description

technical field [0001] The invention relates to a computer, in particular to a deep learning-based fault diagnosis method for asynchronous motors. Background technique [0002] With the advancement of modern science and technology, the development of production systems and the improvement of equipment manufacturing level, motors, as the most commonly used and largest power supply equipment and power machinery in the world, have occupied almost all fields. Obviously, the normal operation of the motor is of great significance to ensure the safe, efficient, agile, high-quality and low-consumption operation in the manufacturing process. Among them, the asynchronous motor is the most widely used and the most demanded motor among various motors. In the total load of the power grid, the consumption of asynchronous motors accounts for more than 60%. It is the most important driving force and driving device in today's industrial production activities and daily life. [0003] At pr...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

Application Information

Patent Timeline
no application Login to view more
IPC IPC(8): G01R31/34G01M15/00G06K9/62
CPCG01R31/343G01M15/00G06F18/24147G06F18/241
Inventor 袁丽英问天宇
Owner HARBIN UNIV OF SCI & TECH
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap