A fault detection method based on orthogonal local preserving projection

A technology for locally maintaining projection and fault detection. It is used in computer parts, special data processing applications, instruments, etc. It can solve problems such as affecting the accuracy and reliability of algorithms, unstable projection vectors, and difficulty in reconstructing data. Computer implementation, simple algorithm, small calculation amount

Inactive Publication Date: 2019-03-26
SHANDONG UNIV OF SCI & TECH
View PDF2 Cites 13 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, the projection vector of the LPP algorithm is not orthogonal, which makes it difficult to reconstruct the data
In addition, the eigendimension is an important parameter of the LPP algorithm, which seriously affects the accuracy and reliability of the algorithm
The eigendimension estimation is too low, and important data features will be projected into the same space; the eigendimension estimation process will cause the projection vector to be unstable

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
  • A fault detection method based on orthogonal local preserving projection
  • A fault detection method based on orthogonal local preserving projection
  • A fault detection method based on orthogonal local preserving projection

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0053] Below in conjunction with accompanying drawing and specific embodiment the present invention is described in further detail:

[0054]A fault detection method based on orthogonal locality preserving projection, its flow is as follows figure 1 As shown, it mainly includes two stages of offline training and online detection. The specific implementation steps are as follows:

[0055] Step 1: Offline training; specifically includes the following steps:

[0056] Step 1.1: collect data in normal operating conditions to form a training data set;

[0057] Use sensors to collect multiple data that reflect the operating status of the system, and form a data matrix X=[x 1 ,x 2 ,...,x N ], where x i ∈R m Represents m variables, the i-th sample, i=1,...,N;

[0058] Step 1.2: Calculate the intrinsic dimension l by using the maximum likelihood estimation method;

[0059] Using the maximum likelihood estimation algorithm, calculate the data point x i Eigendimension at

[0060]...

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 discloses a fault detection method based on orthogonal local preservation projection, belonging to the field of industrial monitoring and fault diagnosis. The method comprises the following steps: collecting process data under normal working conditions of an industrial system to form a training set; adopting Maximum Likelihood Estimation Method to determine the eigendimension. adopting The orthogonal local preservation projection method to compute the projection matrix and construct the monitoring statistics. adopting The kernel density estimation method to determine the threshold. acquiring system real-time process data as a test sample, calculating the statistics of the sample, and compared with the threshold, to determine whether there is a fault. The method of the invention is based on data modeling, does not need accurate analytical model, can keep topological structure and local information between data, has simple algorithm and is easy to realize, and can be widelyapplied in the fields of high-speed railway, chemical industry, manufacturing and the like.

Description

technical field [0001] The invention belongs to the field of industrial monitoring and fault diagnosis, and in particular relates to a fault detection method based on orthogonal local preservation projection. Background technique [0002] With the improvement of people's requirements for the safety and reliability of industrial systems, the problems of process monitoring and fault diagnosis have become the focus of scholars' attention. Traditional data-driven methods mostly use multivariate statistical analysis techniques, such as principal component analysis, partial least squares, independent component analysis, etc., and are widely used in industrial systems. However, these methods aim at global optimization, and cannot maintain the topological structure between data, and lose the local structure information of data, resulting in unsatisfactory fault detection results. The idea of ​​manifold learning can solve this defect, while the data is reduced from a high-dimensiona...

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 Applications(China)
IPC IPC(8): G06K9/62G06F17/50
CPCG06F30/20G06F18/21322G06F18/21324G06F18/214
Inventor 周东华张景欣卢晓钟麦英王建东王友清
Owner SHANDONG UNIV OF SCI & TECH
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