Fault monitoring method based on sequence correlation locality preserving projection algorithm

A technology of local preservation of projection and fault monitoring, applied in program control, electrical test/monitoring, test/monitoring control system, etc., can solve the problem of insufficient mining of training data time series related features.

Active Publication Date: 2020-11-10
NINGBO UNIV
View PDF9 Cites 2 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, the timing-related features of the training data have not been fully exploited

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
  • Fault monitoring method based on sequence correlation locality preserving projection algorithm
  • Fault monitoring method based on sequence correlation locality preserving projection algorithm
  • Fault monitoring method based on sequence correlation locality preserving projection algorithm

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0062] The method of the present invention will be described in detail below in conjunction with the accompanying drawings and specific examples of implementation.

[0063] Such as figure 1 As shown, the present invention discloses a fault monitoring method based on a sequence-correlation locality-preserving projection algorithm. The specific implementation manner of the method of the present invention will now be described in conjunction with a specific implementation case.

[0064] The tested process object is TE process, and the prototype of this process is an actual process flow in Eastman chemical production workshop. Currently, the TE process has been widely used in fault detection research as a standard experimental platform due to its complexity. The whole TE process includes 22 measured variables, 12 manipulated variables, and 19 component measured variables. The collected data are divided into 22 groups, including 1 group of data sets under normal working conditio...

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 monitoring method based on a sequence correlation locality preserving projection algorithm, and aims to infer a brand-new data feature mining algorithm and implement fault monitoring based on the algorithm. Specifically, the method comprises the following steps: combining time sequence correlation maximization and local neighbor structure minimization into a targetfunction; in the solving process, guaranteeing the mutual orthogonal characteristic between the projection transformation vectors; and finally, executing fault monitoring by utilizing the extracted potential features and model errors. Compared with a conventional method, a sequence correlation locality preserving projection algorithm involved in the method is a brand-new feature extraction algorithm, considers the autocorrelation features and local neighbor features at the same time in the projection transformation process, guarantees the orthogonal characteristic of projection transformationvectors, and can mine the hidden useful information in the training data more comprehensively. Therefore, the method provided by the invention is a more preferable fault monitoring method.

Description

technical field [0001] The invention relates to a data-driven fault monitoring method, in particular to a fault monitoring method based on a sequence-correlation part-preserving projection algorithm. Background technique [0002] Real-time monitoring of the operating status of industrial process objects is a direct guarantee to ensure safe production and maintain product quality stability. The design of an integrated automation system for industrial process objects is inseparable from the fault monitoring system. At present, industrial development has entered the stage of informatization construction represented by "big data". The use of sampling data to monitor whether there is a fault in the process operation status has long become one of the research hotspots in the field of industrial automation. Generally speaking, the core idea of ​​the data-driven fault monitoring method is: how to effectively mine the normal data of the process to extract potentially useful informati...

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): G05B23/02
CPCG05B23/0262G05B2219/24065
Inventor 唐俊苗童楚东史旭华
Owner NINGBO 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