Fault diagnosis device based on common information and special information of running video information for electric-arc furnace and method thereof

Patent metadata

Field	Value
Publication number	US-10345046-B2
Application number	US-201715605427-A
Country	US
Kind code	B2
Filing date	May 25, 2017
Priority date	May 25, 2017
Publication date	Jul 9, 2019
Grant date	Jul 9, 2019

Abstract

A fault diagnosis method for an electrical fused magnesia furnace includes steps of: 1) arranging six cameras; 2) obtaining video information by the six cameras and sending the video information to a control center; then analyzing the video information by a chip of the control center; wherein a multi-view-based fault diagnosis method is used by the chip, comprising steps of: 2-1) comparing a difference between two consecutive frame histograms for shots segmentation; 2-2) computing a set of characteristic values for each shot obtained by the step 2-1), and then computing color, texture, and motion vector information; finally, evaluating shot importance via entropy; 2-3) clustering shots together by calculating similarity; 2-4) generating and optimizing a multi-view video summarization with a multi-objective optimization model; and 2-5) providing fault detection and diagnosis; and 3) displaying results of the fault detection and diagnosis on a host computer inter face of the control center.

First claim

What is claimed is:

1. A fault diagnosis method based on common information and special information of running video information for an EFMF (electrical fused magnesium furnace), comprising steps of:
1) arranging six cameras, wherein three of the six cameras are respectively arranged at relative positions of three electrodes above the EFMF and aim at the electrodes of the EFMF, so as to monitor a furnace eruption fault; rest of the six cameras are symmetrically arranged around a furnace body by a 120 degree difference and aim at the furnace body, so as to monitor occurrence of a furnace leaking fault;
2) obtaining video information by the six cameras and sending the video information to a control center; then analyzing the video information by a chip of the control center; wherein in order to simplify a difficulty of analysis and improve a real-time performance of video data analysis, multi-view video summarization technology is introduced, so that industrial process monitoring based on running video information is able to be realized; specifically, a multi-view-based fault diagnosis method is used by the chip, comprising steps of:
2-1) comparing a difference between two consecutive frame histograms for shots segmentation;
2-2) computing a set of characteristic values for each shot obtained by the step 2-1), and then computing color, texture, and motion vector information; finally, evaluating shot importance via entropy;
2-3) clustering shots together by calculating similarity, wherein calculation of the similarity of the shots comprises the similarity of the shots in a mono-view and correlation of the shots in different views;
2-4) generating and optimizing a multi-view video summarization with a multi-objective optimization model; wherein the shot in the shot cluster is either reserved or abandoned so as to obtain the multi-view video summarization with a less number and a shorter length of the shots but contains more fully video information; and
2-5) providing fault detection and diagnosis; and

3) displaying results of the fault detection and diagnosis on a host computer inter face of the control center.

2. The fault diagnosis method, as recited in claim 1 , wherein the step 2-2) specifically comprises a step of computing the color information by a color histogram;
wherein an HSV (hue, saturation and value) color space is used to obtain color histogram information, so as to describe color entropy, wherein:
for a frame f with N color values, a probability of appearance of a i th color value in an image is P i , thus the color entropy is defined as:

E
HSV

⁡

(
f
)

=

∑

i
=
1

N

⁢

⁢

p
i

⁢

log
⁡

(

1
/

p
i

)

(
1
)

wherein Σ i=1 N p i =1 and p i ≥0.

3. The fault diagnosis method, as recited in claim 2 , wherein the step 2-2) specifically comprises a step of computing the texture information by an edge direction histogram;
wherein texture features are extracted using edge direction histogram descriptor; a sobel operator is selected to calculate an edge direction of each pixel; an image space is separated by four lines: horizontal, vertical, 45°, and 135°, in such a manner that the image is divided into eight bins on a center point of the image; then an edge direction information is gathered and an edge direction histogram is obtained; information entropy E EDGE (f) is calculated based on the edge direction histogram of each frame.

4. The fault diagnosis method, as recited in claim 3 , wherein the step 2-2) specifically comprises a step of computing the motion vector information by a motion-related feature vector;
wherein V(t,k) is used to represent a k th bin grey value of the color histogram of a frame t, where 0≤k≤127; a motion-related feature vector is represented by a histogram difference between the frame t and a previous frame t−1, which is determined as

V
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(

Δ
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t

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k

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=

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V
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t
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k

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V
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t
-
1

,
k

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

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2
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E
motion

=

∑

k

Assignees

• Univ Northeastern

Inventors

• Wang Zhenbang
• Zhang Yingwei
• Feng Lin

Classifications

• G06V10/758

  Involving statistics of pixels or of feature values, e.g. histogram matching · CPC title

• G01M3/38

  by using light (G01M3/02 takes precedence) · CPC title

• G06K9/00718

  Physics · mapped topic

• G06T7/001

  using an image reference approach · CPC title

• G06T2207/30232

  Surveillance · CPC title