Industrial big data early failure detection method based on image semi-supervising cost-sensitive

An early failure and cost-sensitive technology, applied in the direction of electrical testing/monitoring, testing/monitoring control systems, instruments, etc., can solve problems such as failure to effectively avoid failure losses, failure to meet fault diagnosis requirements, wrong equipment misdiagnosis costs, etc.

Active Publication Date: 2018-04-17
NORTHEASTERN UNIV
View PDF5 Cites 11 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Due to the different degrees of harm, the cost of misdiagnosis of equipment is not equal, and the cost of safety hazards and economic losses required to misdiagnose the fault state as a normal state is often greater than the cost of the opposite situation.
In addition, since the acquisition of fault samples is at the cost of a certain degree of equipment damage, the number of fault samples will be much less than that of normal samples. The conclusion of the diagnostic method is more inclined to the judgment of the normal state, and cannot effectively avoid the loss caused by the fault
Therefore, the goal of minimizing the misclassification rate cannot meet the actual fault diagnosis requirements.

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
  • Industrial big data early failure detection method based on image semi-supervising cost-sensitive
  • Industrial big data early failure detection method based on image semi-supervising cost-sensitive
  • Industrial big data early failure detection method based on image semi-supervising cost-sensitive

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0092] The specific implementation manners of the present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments. The following examples are used to illustrate the present invention, but are not intended to limit the scope of the present invention.

[0093] Fused magnesia furnace is one of the main equipment used to produce fused magnesia. With the development of smelting technology, fused magnesia furnace has been widely used in the magnesia production industry. Electric smelting magnesia furnace is a kind of smelting furnace with electric arc as heat source. Its heat is concentrated and can smelt magnesia well. The smelting process of the electric fused magnesium furnace goes through the process stages of melting, separation, purification and crystallization. The industrial process of smelting magnesium furnace is as follows: figure 1 As shown, the equipment used includes a transformer 1, a short network 2, an elec...

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 provides an industrial big data early failure detection method based on image semi-supervising cost-sensitive, and relates to the technical field of failure detection and diagnosis. Theindustrial big data early failure detection method based on image semi-supervising cost-sensitive includes the steps: acquiring the data of the industrial process, and utilizing an image semi-supervising tag propagation method to update tags without marked data and delineating the suspected early failure; for the suspected early failure points, performing cost-sensitive Bayesian classification, and completing secondary updating of the tags of the suspected early failure points; and finally establishing an EDC-SVM (Example dependent cost-sensitive support vector machine) classifier, and performing on-line failure diagnosis on the industrial process. The industrial big data early failure detection method based on image semi-supervising cost-sensitive takes minimization of the diagnosis costas the failure diagnosis target, divides the suspected early failure, and solves the problem that the industrial failure detection error classification cost is high. And at the same time, on the premise of guaranteeing accuracy of classification, the industrial big data early failure detection method based on image semi-supervising cost-sensitive can reduce the misjudgment cost during the failuredetection process, and can improve safety of the industrial process.

Description

technical field [0001] The invention relates to the technical field of fault detection and diagnosis, in particular to a graph-based semi-supervised cost-sensitive early fault detection method for industrial big data. Background technique [0002] With the rapid development of modern industry, the production equipment in modern enterprises is increasingly large-scale, continuous, high-speed and automatic. The structure and composition of equipment are very complicated, the production scale is very large, and the connection between various departments is also particularly close. The actual production process is linear, nonlinear, time-invariant, time-varying, etc. For the characteristics of different production processes, different fault monitoring methods should be selected, so as to effectively detect faults. [0003] Traditional classification algorithms usually aim at minimizing the global classification misclassification rate, and assume that the misclassification costs ...

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
CPCG05B23/0243G05B2219/24065
Inventor 张颖伟郑肇默冯琳
Owner NORTHEASTERN 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