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Fault Diagnosis Method of Rolling Bearing Based on Sparse Encoder and Support Vector Machine

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

Active Publication Date: 2018-09-04
YANSHAN UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • 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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  • Fault Diagnosis Method of Rolling Bearing Based on Sparse Encoder and Support Vector Machine
  • Fault Diagnosis Method of Rolling Bearing Based on Sparse Encoder and Support Vector Machine
  • Fault Diagnosis Method of Rolling Bearing 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 experimental platform includes a 2-horsepower motor (left side) (1h=746w), a torque sensor (middle), a dynamometer (right side) and electronic control equipment. The test bench includes the drive end bearing and the fan end bearing, and the acceleration sensor is installed at the 12 o'clock position of the drive end and the fan end of the motor housing respectively. The vibration signal is collected by a 16-channel DAT recorder, and the sampling frequency of the drive end bearing fault data is 48,000 points per second. In this experiment, we choose the driving end (bearing) as the research object. Under the condition that the motor load is 3HP, the data of bearing fault mode as no...

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Abstract

A rolling bearing fault diagnosis method based on sparse encoder and support vector machine, adopts the deep learning autonomous cognition method based on cascaded sparse autoencoder, automatically extracts the essential features of the input data from simple to complex, from low-level to high-level, Automatically excavate the rich information hidden in the known data; use deep learning to extract features and combine the learned features of the two layers together to form the input of the support vector machine, and the working status and fault of the rolling bearing can be judged through the classification of the support vector machine Types of. The method of the invention can improve the efficiency and accuracy of fault feature extraction.

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 status 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 are fatigue spalling, wear, plastic deformation, corrosion, fracture, gluing, 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 fault characteristics of bearings has great practical significance...

Claims

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Application Information

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