Fault diagnosis method for induction motor of electric automobile based on broad learning

A technology for induction motors and electric vehicles, applied in motor generator testing, computer parts, instruments, etc., can solve the problems that the diagnosis system model cannot be modified, the application is limited, and the motor fault diagnosis system remains unchanged.

Inactive Publication Date: 2018-12-07
ZHUHAI COLLEGE OF JILIN UNIV +1
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

This means that once the model is trained, the diagnostic system model cannot be modified
One of the main challenges in fault diagnosis

Method used

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  • Fault diagnosis method for induction motor of electric automobile based on broad learning
  • Fault diagnosis method for induction motor of electric automobile based on broad learning
  • Fault diagnosis method for induction motor of electric automobile based on broad learning

Examples

Experimental program
Comparison scheme
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Example Embodiment

[0099] Example

[0100] 1) Experimental platform

[0101] In the implementation, the motor and electrical technology test bench TCDJ 03A is used as the experimental platform. The platform provides three-phase AC voltage to the three-phase induction motor and DC voltage to the generator. The workbench can simultaneously measure motor voltage, motor current and motor speed. Three-phase asynchronous motor star connection. Connect the generator fixed on the guide rail with the three-phase asynchronous motor. The generator is set to load. The load force can be adjusted by the load resistance. The oscilloscope GDS-2202A is used to measure the three-phase induction motor current and store the current into digital memory. Use a computer (Samsung R429) to connect with a microphone (Lenovo P121) to obtain audio data. The microphone is clipped to get sound waves. According to the measurement method proposed by GB3806-81, the distance between the measurement point and the reflecti...

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Abstract

The invention discloses a fault diagnosis method for an induction motor of an electric automobile based on broad learning. According to the method, an incremental broad learning (IBL) method is adopted to diagnose a fault of the induction motor, the structure of IBL is simple, and the network can be effectively trained and retrained. Feature extraction (empirical mode decomposition and sample entropy), incremental broad learning and singular value decomposition are combined to form an intelligent diagnosis method for the induction motor of the electric automobile. Experimental results show that the method is superior to other algorithms in fault diagnosis for the induction motor of the electric automobile. In addition, the IBL simplified by singular value decomposition (SVD) is smaller inerror and higher in system stability than the previous IBL.

Description

technical field [0001] The invention relates to the field of motor fault diagnosis, and more particularly, to a fault diagnosis method for electric vehicle induction motors based on width learning. Background technique [0002] As we all know, the means of transportation is one of the very important means of transportation for human beings. There are two main driving methods for vehicles: one is a fuel engine and the other is an electric motor. However, the gasoline engine was rejected by the government due to pollution problems. Several countries, including the Netherlands, Germany, France and the UK, have previously announced timetables for banning the sale of gasoline vehicles. With the increasingly serious oil shortage and environmental pollution, the development of fuel vehicles is restricted. In the next few years, the research and application of electric vehicles will become more and more popular. The key component of an electric vehicle is the electric motor. El...

Claims

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Application Information

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IPC IPC(8): G06K9/62G01R31/34
CPCG01R31/34G06F18/213G06F18/24G06F18/214
Inventor 江赛标王百键司玉娟李嘉张亦聪
Owner ZHUHAI COLLEGE OF JILIN UNIV
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