Looking for breakthrough ideas for innovation challenges? Try Patsnap Eureka!

Rolling bearing fault diagnosis method

A rolling bearing and fault diagnosis technology, which is applied in the testing of machines/structural components, testing of mechanical components, instruments, etc., can solve problems such as influence and achieve the effect of improving accuracy

Pending Publication Date: 2021-01-15
KUNMING UNIV OF SCI & TECH
View PDF0 Cites 10 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

The extreme learning machine is a single-layer feed-forward neural network. It does not need to repeatedly adjust the parameters of the hidden layer, but the random selection of the connection weight between the input layer and the hidden layer and the bias value of the hidden layer will affect its Stability and classification accuracy

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
  • Rolling bearing fault diagnosis method
  • Rolling bearing fault diagnosis method
  • Rolling bearing fault diagnosis method

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0037] Embodiment 1: as figure 1 As shown, a rolling bearing fault diagnosis method first uses VMD to decompose the vibration signal of the rolling bearing into a series of modal components, calculate the sample entropy of each modal component and input it as a feature vector. Then, the cuckoo search algorithm is used to optimize the weight and threshold of the extreme learning machine, and the CS-ELM model is established. Finally, the sample entropy eigenvalue is input into the model to classify and identify the fault types of bearings under different working conditions.

[0038] The specific steps are:

[0039] 1) Collect the vibration signals of normal bearings, inner ring fault bearings, outer ring fault bearings, and rolling element faults of rolling bearings under a certain load state;

[0040] 2) Decompose the bearing fault vibration signal f(t) under different working conditions into k eigenmodes u k , while satisfying that the sum of the estimated bandwidths of eac...

Embodiment 2

[0063] Embodiment 2: In this embodiment, the method as shown in Embodiment 1 is adopted to carry out the fault diagnosis of the bearing, and the specific implementation steps are as follows:

[0064] Now install the acceleration sensor on the drive end of the rolling bearing. Among them, the bearing damage diameter is 0.1778mm, the set speed is 1797r / min, and the sampling frequency is 12kHz, and the different vibration signals of the rolling bearing under inner ring fault, outer ring fault, rolling element fault and normal state are measured respectively. Such as image 3 The waveform diagram shown.

[0065] Take 200 sets of data for each bearing state signal, 170 sets of data randomly selected from each state sample data, that is, as a training sample, the remaining four states of normal, inner ring fault, outer ring fault, and rolling element fault Each set of data of the bearing vibration signal is used as a test sample.

[0066] According to the principle of avoiding mo...

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 relates to a rolling bearing fault diagnosis method, and belongs to the technical field of mechanical equipment fault diagnosis. Firstly, variation modal decomposition is used for decomposing a vibration signal of a rolling bearing into a series of modal components, and the sample entropy of each modal component is calculated and serves as feature vector input; and then the weight and threshold of the extreme learning machine is optimized by adopting a cuckoo search algorithm, and a CSEELM model is established. And finally, the sample entropy characteristic value is input into the model, and classification and identification are carried out on different working condition fault types of the bearing. According to the invention, signal mode aliasing can be effectively overcome,and the accuracy of fault identification is improved.

Description

technical field [0001] The invention relates to a rolling bearing fault diagnosis method, which belongs to the category of mechanical fault diagnosis. Background technique [0002] Rolling bearings are the most widely used parts of heavy rotating machinery. Problems in the operating state will cause the shutdown of the entire mechanical equipment and even casualties, resulting in serious economic losses. Therefore, real-time monitoring and fault diagnosis of the health status of rolling bearings are of great significance. [0003] The vibration signals of rolling bearings are usually characterized by non-stationary and nonlinear characteristics, which restrict the effective extraction of fault features. Traditional feature extraction methods have modal aliasing, boundary effects, over-decomposition, etc. The extreme learning machine is a single-layer feed-forward neural network. It does not need to repeatedly adjust the parameters of the hidden layer, but the random select...

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): G06K9/00G01M13/045
CPCG01M13/045G06F2218/08G06F2218/12
Inventor 王海瑞王椿晶
Owner KUNMING UNIV OF SCI & TECH
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Patsnap Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Patsnap Eureka Blog
Learn More
PatSnap group products

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

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap|About US| Contact US: help@patsnap.com