Prediction method for mold breakout based on feature vectors and hierarchical clustering

Patent metadata

Field	Value
Publication number	US-11105758-B2
Application number	US-201916761474-A
Country	US
Kind code	B2
Filing date	Aug 12, 2019
Priority date	Dec 11, 2018
Publication date	Aug 31, 2021
Grant date	Aug 31, 2021

Abstract

A prediction method for mold breakout based on feature vectors and hierarchical clustering is disclosed, which comprises: respectively extracting temperature feature vectors of historical data under sticking breakout and normal conditions and on-line actually measured data to establish a feature vector sample set; performing normalization and hierarchical clustering on the sample set; and checking and judging whether the feature vectors extracted on line belong to a breakout cluster, and then identifying and predicting mold breakout. The method avoids the steps of tedious adjustment and modification of alarm threshold and other parameters, overcomes the artificial dependence of the previous breakout prediction method, has good robustness and mobility; and through temperature feature extraction, achieves accurate identification of sticking breakout temperature patterns, avoids missing alarms and significantly reduces the number of times of false alarms, and greatly reduces the data calculation amount and calculation time, guaranteeing the timeliness of on-line prediction.

First claim

The invention claimed is:

1. A prediction method for mold breakout based on feature vectors and hierarchical clustering comprising the following parts: respectively extracting temperature feature vectors of historical data of sticking breakout and normal conditions and on-line actually measured data, and establishing a feature vector sample set; performing normalization and hierarchical clustering on the sample set; and checking and judging whether the feature vectors extracted on line belong to a breakout cluster, and then identifying and predicting mold breakout comprising the following steps:
first step of extracting sticking breakout feature vectors comprising:
(1) acquiring historical temperature data of sticking breakout by marking the time when the temperature of a first row of thermocouples in a thermocouple column where a sticking position is located is the maximum, and selecting the temperature data within a first M seconds and a last N−1 seconds, M+N seconds in total; and
(2) extracting and constructing temperature feature vectors of the first row and a second row of thermocouples;

second step of extracting normal condition feature vectors comprising:
(1) acquiring historical temperature data under normal conditions by randomly intercepting temperature data at continuous M+N seconds; and
(2) extracting and constructing temperature feature vectors of the first and second rows of thermocouples;

third step of extracting on-line real-time temperature feature vectors comprising:
(1) collecting and acquiring temperature data of thermocouples of each row and each column on loosed wide face, fixed wide face, left narrow face, and right narrow face copperplates of a mold within the current time and previous M+N−1 seconds, M+N seconds in total in real time; and
(2) extracting and constructing temperature feature vectors of the first and second rows of thermocouples;

fourth step of establishing a feature vector library comprising:
(1) establishing a feature vector sample library D based on the sticking breakout, normal condition and on-line actually measured temperature feature extracted in the first step, the second step and the third step; and
(2) performing normalization on the feature vector sample library D, to obtain a feature vector sets, and recording a normalized on-line actually measured temperature feature as S new
the normalization method for the feature vector is as follows:

x_nor
ij

=

x
ij

-

x
jmin

x
jmax

-

x
jmin

,

i
=
1

,
2
,
…
⁢

,


S


,

j
=
1

,
2
,
…
⁢

,
5

where x ij , represents the value of the j-dimensional feature of the ith feature vector in the feature vector set s, x jmax and x jmin respectively represent a maximum and a minimum of the j-dimensional feature of all feature vectors, and S represents the total number of vectors in the feature vector set s;

fifth step of performing hierarchical clustering on feature vectors comprising:
(1) performing hierarchical clustering on the feature vector sets obtained in the fourth step, the specific process including:
1.1) taking each vector sin the feature vector set S as an initial cluster C i ={S i }, and establishing a cluster set C={C 1 , C 2 , . . . , C k }, where s i , represents the ith vector in S, C i , represents the ith cluster, i=1,2, . . . , k, k represents the total number of vectors in the feature vector set s
1.2) calculating and determining a distance between any two clusters C p and C q in the cluster set C:
d ( C p ,C q )=min(dist( C pi ,C qj ))

where C, represents the ith feature vector in the cluster C p , C qj represents the jth feature vector in the cluster Cq, and dist(C pi , C qj ) represents the Euclidean distance between the feature vectors C pi and C qj ; and calculating the distance between any two vectors in the clusters C p and C q and taking the minimum distance min as the distance between the clusters C p and C q ;
1.3) marking two clusters C m and C n between which the distance is the minimum calculated in the step 1.2, merging C m and C n into a new cluster C {m,n} and adding same to the set C, and deleting the original clusters C m and C n , so after cluster addition and deletion, the total number of clusters in the set C is reduced by one at this time;
1.4) performing steps 1.2-1.3 in a loop, when there are only two clusters in the cluster set C, ending the loop, and completing the clustering process;
(2) checking whether the clustering result meets the following judgement condition, that is:
more than 90% of all sticking breakout feature vectors belong to the same cluster, and the percentage of the normal condition feature vectors in the cluster is less than 20%;
if this condition is met, recording this cluster as a breakout cluster C breakout and recording the other cluster as a normal condition cluster C normal ; otherwise, performing the fifth step again until the clustering result of the feature vector set composed of breakout, normal condition, and actually measured temperature feature meets the above judgement condition;

sixth step of identifying breakout and issuing alarm comprising:
judging whether the new feature vector s new , belongs to the cluster C breakout , if so, issuing a breakout alarm; otherwise, continuing to perform the third through sixth steps;
the temperature feature extraction methods involved in the first step (2), the second step (2) and the third step (2) being identical, extracting features of change in the temperature of the same column of thermocouples in a casting direction under different working conditions by using each column of thermocouples as a unit, specifically including:
1st_Rising_Amplitude: first row temperature rising amplitude;
1st_Rising_V_Max: first row temperature rising velocity maximum;
1st_Falling_V_Ave: first row temperature falling velocity average;
2nd_Rising_V_Max: second row temperature rising velocity maximum;
1st_2nd_Time_Lag: temperature rising time lag, that is, time interval between the time when the temperature of the second row of thermocouple starts to rise and the time when the temperature of the first row of thermocouple starts to rise;
and thus constructing a feature vector:

s=[1st_Rising_Amplitude, 1st_Rising_V_Max, 1st_Falling_V_Ave, 2nd_Rising_V_Max, 1st_2nd_Time_Lag].

2. The prediction method for mold breakout based on feature vectors and hierarchical clustering according to claim 1 , wherein the pred

Assignees

• Univ Dalian Tech

Inventors

• Wang Xudong
• Duan Haiyang
• Yao Man

Classifications

• B22D11/166 Primary

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• G01N25/72 Primary

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• G06F18/231

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• G06V10/40

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