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

Method and system of failure prediction

A fault prediction and fault direction technology, applied in electrical testing/monitoring, etc., can solve the problem that fault estimation technology cannot predict faults very accurately

Active Publication Date: 2015-05-20
BEIJING INFORMATION SCI & TECH UNIV
View PDF5 Cites 0 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

In recent studies, the fault estimation technique based on Principal Component Analysis (PCA) has been successfully used in fault prediction, but for nonlinear data, the fault estimation technique based on PCA is not very accurate. Therefore, it is necessary to propose a new fault prediction scheme for nonlinear data

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
  • Method and system of failure prediction
  • Method and system of failure prediction
  • Method and system of failure prediction

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0036] The present invention will be described in further detail below in conjunction with the accompanying drawings.

[0037] The Kernel Principal Component Analysis (KPCA) method is a new algorithm derived from the PCA method to study the nonlinear characteristics of data. The KPCA method is a nonlinear method based on the PCA method. It maps the input data x to a high-dimensional feature space F through a certain nonlinear mapping function φ( ), and then processes the data in F according to the linear principal component analysis method. . Transform the nonlinear problem in the original measurement space into a linear problem in the feature space.

[0038] assuming x 1 , x 2 ,...,x n ∈ R m are n m-dimensional column vector training samples for kernel principal component analysis learning. Let the nonlinear mapping be φ, the original data x i (i=1,2,...,n) in the mapping space F high The image in φ(x i ).

[0039] An n×n-dimensional kernel matrix K can be defined, ...

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 method and a system of failure prediction. The method of the failure prediction comprises calculating a kernel principal element, and detecting a failure according to control limits. A method based on kernel principal component analysis (KPCA) reconfiguration is adopted in the failure prediction aiming at rotating machinery, the nonlinear problem of process data can be solved well, a failure direction is obtained from the data implying failures, a failure amplitude value is estimated, the multi-dimensional character of the failures is considered, and an accurate failure prediction result can be obtained.

Description

technical field [0001] The invention relates to the field of fault prediction, in particular to a fault prediction method and system. Background technique [0002] With the development of science and technology and industry, rotating machinery equipment is developing towards large-scale, high-speed and complex. Therefore, the requirements for the reliability, continuity, and economy of equipment and systems are increasing day by day in the production of enterprises. On the basis of effective diagnosis and solutions for equipment and system failures in the past, it is further required that only minor abnormalities occur in the failure When there is a sign, the fault can be predicted and corresponding emergency measures can be put forward. There are various methods of fault prediction, among which statistical process monitoring technology has been developed for more than 20 years and is widely used in fault detection, diagnosis and estimation of industrial processes. In rece...

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
Patent Type & Authority Patents(China)
IPC IPC(8): G05B23/02
Inventor 马洁李钢陈默徐嘉楠
Owner BEIJING INFORMATION SCI & TECH UNIV
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