Dynamic industrial process fault diagnosis method based on GRU depth neural network

A deep neural network, industrial process technology, applied in the field of dynamic industrial process fault diagnosis, can solve problems such as aggravating the disaster of dimensionality and instability of feature extraction methods

Active Publication Date: 2019-03-12
UNIV OF SHANGHAI FOR SCI & TECH
View PDF4 Cites 6 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, these vector-based augmentation methods may exacerbate the "curse of dimensionality" (a phenomenon in which the computation increases exponentially with the increase of dimensionality in problems invo

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
  • Dynamic industrial process fault diagnosis method based on GRU depth neural network
  • Dynamic industrial process fault diagnosis method based on GRU depth neural network
  • Dynamic industrial process fault diagnosis method based on GRU depth neural network

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0042] Example figure 1 Shown, the present invention is based on the dynamic industrial process fault diagnosis method of GRU deep neural network and comprises the following steps:

[0043] Step 1. Suppose there are n groups of original experimental data X=[x 1 ,x 2 ...x n ], Set the time step T as 3, use the moving window to preprocess the original experimental data, and divide the original n groups of experimental data into [x 1 ,x 2 ,x 3 ], [x 2 ,x 3 ,x 4 ], [x 3 ,x 4 ,x 5 ] sequence unit, there are n-T sequence units;

[0044] in, Indicates the dimension of each group of original experimental data x, is a mathematical symbol, representing the real phasor space, d x Represents the characteristic number of x;

[0045] Step 2, the sequence unit X (1) =[x 1 ,x 2 ,x 3 ], X (2) =[x 2 ,x 3 ,x 4 ]…X (m) =[x n-2 ,x n-1 ,x n ] input to the GRU deep neural network optimized by the batch normalization algorithm, each input sequence unit X (i) Obtain the...

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 dynamic industrial process fault diagnosis method based on a GRU depth neural network. The method divides original data into a plurality of sequence units as the input of theGRU, a GRU network is established through a batch normalization algorithm, extract dynamic characteristics can be effectively extracted from the sequence units, by adopting a softmax regression method, faults are classified according to the dynamic characteristics extracted by the GRU, and the probability interpretation of the classification is provided, so that the diagnosis result is further accurate, the problem of dimension disaster is avoided, and the accuracy of the fault diagnosis of the dynamic industrial process is improved.

Description

technical field [0001] The invention relates to a dynamic industrial process fault diagnosis method based on a GRU deep neural network. Background technique [0002] With the development of modern industrial technology and process control mechanisms, industrial processes have become more and more complex. To improve industrial process safety and product quality, industrial process monitoring and fault diagnosis have received extensive attention in the past decades. Data-driven multivariate statistical process monitoring (MSPM) has been widely used in the monitoring of industrial process operations and production results. Compared with knowledge-based and model-based methods, MSPM methods are easier to set up. Therefore, MSPM models such as Principal Component Analysis (PCA), Independent Component Analysis (ICA) are widely used in industrial process monitoring and fault diagnosis. The framework of traditional fault diagnosis mainly includes two steps: 1) feature extraction...

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): G05B23/02
CPCG05B23/024
Inventor 袁菁田颖赵湉陆韬游欣宇胡田
Owner UNIV OF SHANGHAI FOR 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