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Rolling bearing fault diagnosis method based on sparse encoder and support vector machine

A support vector machine and sparse coding technology, which is applied in the field of rolling bearing parameter diagnosis, can solve problems such as time-consuming, difficult to find models, time-consuming and labor-intensive problems, and achieve the effect of improving accuracy and excellent feature learning ability

Active Publication Date: 2016-11-16
YANSHAN UNIV
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  • Abstract
  • Description
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  • Application Information

AI Technical Summary

Problems solved by technology

However, these methods of extracting features are all manually extracted, and will consume a lot of time in the calculation and testing of fault mode recognition
In addition, manually selecting features is not only time-consuming and laborious, but also the features that need to be extracted are different when studying different objects
Due to the diversity of features, it is difficult to find a unified model suitable for different objects

Method used

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  • Rolling bearing fault diagnosis method based on sparse encoder and support vector machine
  • Rolling bearing fault diagnosis method based on sparse encoder and support vector machine
  • Rolling bearing fault diagnosis method based on sparse encoder and support vector machine

Examples

Experimental program
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Effect test

Embodiment example 1

[0066] Taking the bearing data of Western Reserve University in the United States as an example, the implementation method of rolling bearing fault diagnosis based on SSAE model deep learning and particle swarm support vector machine is explained.

[0067] (1) Test data

[0068] The rolling bearing experiment platform includes a 2 horsepower motor (left side) (1h=746w), a torque sensor (middle), a power meter (right side) and electronic control equipment. The test bench includes a drive end bearing and a fan end bearing, and the acceleration sensor is respectively installed at the 12 o'clock position of the drive end and the fan end of the motor housing. The vibration signal is collected by a 16-channel DAT recorder, and the sampling frequency of the drive end bearing failure data is 48,000 points per second. In this experiment, we selected the drive end (bearing) as the research object. When the motor load is 3HP, the bearing failure modes are selected as normal, inner ring fail...

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Abstract

The invention discloses a rolling bearing fault diagnosis method based on a sparse encoder and a support vector machine. A deep learning and autonomous cognition method based on a stacked sparse automatic encoder is adopted, essential characteristics of input data are automatically extracted from simplicity to complexity and from a low level to a high level, rich information hidden in known data is automatically dug out, the deep learning is adopted for extracting the characteristics and features learnt by two layers are integrated together to form input of the support vector machine, and through classification by the support vector machine, the working state and the fault type of the rolling bearing can be judged. The method of the invention can improve the fault characteristic extraction efficiency and the accuracy.

Description

Technical field [0001] The invention relates to the field of rolling bearing parameter diagnosis, in particular to a rolling bearing fault diagnosis method based on a sparse encoder and a support vector machine. Background technique [0002] Rolling bearing is one of the most widely used mechanical parts, and it is also one of the most easily damaged components in mechanical equipment. Its operating state directly affects the function of the entire equipment. According to incomplete statistics, in rotating machinery using rolling bearings, about 30% of mechanical failures are caused by bearings. The causes of bearing failure include fatigue peeling, wear, plastic deformation, corrosion, fracture, bonding, cage damage, etc. If the early failure of the bearing is not diagnosed in time, it will cause serious failure of the machinery and equipment, resulting in huge economic losses. Therefore, diagnosing the early failure characteristics of the bearing has great practical significa...

Claims

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Application Information

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IPC IPC(8): G01M13/04G06K9/62
CPCG01M13/045G06F18/2411
Inventor 时培明梁凯赵娜韩东颖
Owner YANSHAN UNIV
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