Prediction of residual service life of gas turbine bearings based on support vector regression

A technology of support vector regression and life prediction, which is applied in computer parts, computer-aided design, special data processing applications, etc., can solve problems such as easy to fall into misunderstandings, complex model establishment, poor model interpretability, etc., to achieve efficient prediction, improve The effect of forecast accuracy

Inactive Publication Date: 2019-03-15
SHANGHAI JIAO TONG UNIV +1
View PDF5 Cites 15 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

The disadvantage of the former is that the establishment of the model is very complicated, and it is easy to ignore some influencing factors, resulting in inaccurate prediction results.
Compared with the physical model,

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
  • Prediction of residual service life of gas turbine bearings based on support vector regression
  • Prediction of residual service life of gas turbine bearings based on support vector regression
  • Prediction of residual service life of gas turbine bearings based on support vector regression

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0026] Such as figure 1 As shown, it is a method for predicting the remaining service life of gas turbine bearings based on support vector regression involved in this embodiment, and its specific steps include:

[0027] Step 1) Use the acceleration sensor to collect the acceleration vibration signal of the gas turbine bearing from normal operation to complete degradation, including the vibration acceleration in the horizontal direction of the bearing and the vibration acceleration in the vertical direction of the bearing. The data unit of the bearing acceleration is the gravitational acceleration g. With the continuous degradation of the bearing, the amplitude of the vibration signal of the bearing gradually increases until the vibration acceleration exceeds 20g, and the bearing is completely degraded.

[0028] The data collection is completed by the acceleration sensors arranged on both sides of the bearing. The acceleration sensor detects the acceleration vibration signal of...

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

A method for predicting residual service life of gas turbine bearing based on support vector regression By collecting the health status data of gas turbine bearing and extracting the time-domain and frequency-domain features after preprocessing, Then the time-domain and frequency-domain features are fused by the data fusion tool through the principal component analysis method, and the low-dimensional feature indices which characterize the bearing degradation are obtained and used to train the residual service life prediction model based on support vector regression. Finally, the residual service life prediction model is used to predict the bearing life in real time. The prediction error of the invention is lower than the prediction result of the ordinary support vector regression model andthe neural network model, and the prediction result precision is high.

Description

technical field [0001] The invention relates to a technology in the field of thermal power generation, in particular to a method for predicting the remaining service life of a gas turbine bearing based on support vector regression. Background technique [0002] The bearing parts of the gas turbine are an important part of the operation of the gas turbine. Predicting the remaining life of bearings has become a hot research issue in the academic field of prediction. The methods used to predict the remaining life of gas turbine bearings are mainly divided into two categories. On the one hand, the mechanical model of the working state of the bearing is established, and the working state of the bearing is analyzed through various factors such as material, speed, frequency, temperature, and humidity. , to predict the remaining life of the bearing through the physical model. On the other hand, by setting various sensors to detect the state monitoring data of the bearing during op...

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): G06K9/00G06F17/50
CPCG06F2119/04G06F30/17G06F2218/12
Inventor 夏唐斌徐文华宋亚严春华朱启杰郑宇
Owner SHANGHAI JIAO TONG UNIV
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