WO2020119156A1 - 一种基于特征向量和层次聚类的结晶器漏钢预报方法 - Google Patents

一种基于特征向量和层次聚类的结晶器漏钢预报方法 Download PDF

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WO2020119156A1
WO2020119156A1 PCT/CN2019/100130 CN2019100130W WO2020119156A1 WO 2020119156 A1 WO2020119156 A1 WO 2020119156A1 CN 2019100130 W CN2019100130 W CN 2019100130W WO 2020119156 A1 WO2020119156 A1 WO 2020119156A1
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temperature
cluster
feature vector
feature
vectors
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王旭东
段海洋
姚曼
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Dalian University of Technology
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    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B22—CASTING; POWDER METALLURGY
    • B22D—CASTING OF METALS; CASTING OF OTHER SUBSTANCES BY THE SAME PROCESSES OR DEVICES
    • B22D11/00—Continuous casting of metals, i.e. casting in indefinite lengths
    • B22D11/16—Controlling or regulating processes or operations
    • B22D11/166—Controlling or regulating processes or operations for mould oscillation
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B22—CASTING; POWDER METALLURGY
    • B22D—CASTING OF METALS; CASTING OF OTHER SUBSTANCES BY THE SAME PROCESSES OR DEVICES
    • B22D11/00—Continuous casting of metals, i.e. casting in indefinite lengths
    • B22D11/04—Continuous casting of metals, i.e. casting in indefinite lengths into open-ended moulds
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B22—CASTING; POWDER METALLURGY
    • B22D—CASTING OF METALS; CASTING OF OTHER SUBSTANCES BY THE SAME PROCESSES OR DEVICES
    • B22D11/00—Continuous casting of metals, i.e. casting in indefinite lengths
    • B22D11/04—Continuous casting of metals, i.e. casting in indefinite lengths into open-ended moulds
    • B22D11/051—Continuous casting of metals, i.e. casting in indefinite lengths into open-ended moulds into moulds having oscillating walls
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B22—CASTING; POWDER METALLURGY
    • B22D—CASTING OF METALS; CASTING OF OTHER SUBSTANCES BY THE SAME PROCESSES OR DEVICES
    • B22D11/00—Continuous casting of metals, i.e. casting in indefinite lengths
    • B22D11/16—Controlling or regulating processes or operations
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B22—CASTING; POWDER METALLURGY
    • B22D—CASTING OF METALS; CASTING OF OTHER SUBSTANCES BY THE SAME PROCESSES OR DEVICES
    • B22D11/00—Continuous casting of metals, i.e. casting in indefinite lengths
    • B22D11/16—Controlling or regulating processes or operations
    • B22D11/18—Controlling or regulating processes or operations for pouring
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B22—CASTING; POWDER METALLURGY
    • B22D—CASTING OF METALS; CASTING OF OTHER SUBSTANCES BY THE SAME PROCESSES OR DEVICES
    • B22D46/00—Controlling, supervising, not restricted to casting covered by a single main group, e.g. for safety reasons
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N25/00—Investigating or analyzing materials by the use of thermal means
    • G01N25/72—Investigating presence of flaws
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/10—Complex mathematical operations
    • G06F17/18—Complex mathematical operations for evaluating statistical data, e.g. average values, frequency distributions, probability functions, regression analysis
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/23—Clustering techniques
    • G06F18/231—Hierarchical techniques, i.e. dividing or merging pattern sets so as to obtain a dendrogram
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/20—Design optimisation, verification or simulation
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N20/00—Machine learning
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/40—Extraction of image or video features
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2113/00—Details relating to the application field
    • G06F2113/22—Moulding
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2119/00—Details relating to the type or aim of the analysis or the optimisation
    • G06F2119/08—Thermal analysis or thermal optimisation
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2119/00—Details relating to the type or aim of the analysis or the optimisation
    • G06F2119/14—Force analysis or force optimisation, e.g. static or dynamic forces

Definitions

  • the invention belongs to the technical field of iron and steel metallurgy continuous casting detection, and relates to a method for predicting steel leakage based on feature vectors and hierarchical clustering.
  • Bonding leakage steel refers to the rupture of the thin primary shell near the meniscus during the continuous casting process. After the molten steel oozes out, it will contact with the copper plate of the mold to form a bond. As the mold vibrates and the billet moves down, the bond repeatedly tears- Healed and continued to move down. When it moved out of the crystallizer outlet, it lost the supporting constraints of the copper plate, and the molten steel overflowed, causing steel leakage. Steel leakage not only endangers the safety of on-site operators, but also severely damages continuous casting equipment. At the same time, continuous casting production will be forced to be interrupted, and equipment maintenance and production costs will increase significantly. Therefore, online monitoring and forecasting of steel leakage is always the top priority of continuous casting process control, which is of great significance to ensure the smooth progress of production.
  • the existing method for predicting steel leakage mainly consists of embedding and installing a thermocouple on the copper plate of the crystallizer, and monitoring and judging whether there is adhesion between the slab and the copper plate according to the temperature change of the thermocouple.
  • Practice has shown that the rate of change of thermocouple temperature with time and its amplitude when bonding occurs are significantly different from normal operating conditions. Therefore, the common characteristics of thermocouple temperature and its rate and amplitude can be extracted and summarized, combined with logic Judgment or neural network method to distinguish and identify typical temperature characteristics of bonding, and online prediction of mold leakage.
  • the steel leakage prediction method based on logical judgment has a strong dependence on continuous casting equipment, process parameters and physical property parameters.
  • the threshold value changes greatly , Leading to a significant increase in the false positive rate and false negative rate; the neural network method has higher requirements for learning and training samples.
  • the sample is incomplete or invalid, it will seriously affect its prediction effect, and the model's ability to migrate is low.
  • a certain surplus will be reserved when designing the forecast algorithm or setting the alarm threshold.
  • the forecast threshold needs to be frequently debugged and corrected manually. If the setting is improper or adjusted If it is not timely, it is difficult to accurately identify and eliminate false alarms, and even lead to omissions. It is a common problem faced by the current steel leakage forecast system.
  • Patent CN106980729A discloses a method for predicting continuous casting steel leakage based on a hybrid model. This method collects and stores real-time data of all thermocouple temperatures on site, and determines whether the time series of each thermocouple temperature change is consistent with that of bonded steel leakage Temperature change waveform, save the judgment result as Y(i,j,t), if Y(i,j,t) is within the set threshold value, it is marked as thermocouple abnormal, and the current thermocouple line and the previous line are counted For the number of abnormal thermocouples, compare the total number of abnormal thermocouples output with the thresholds for the number of thermocouples for sticking warnings and sticking alarms.
  • This method focuses on the temperature change of a single thermocouple and the number of superimposed thermocouples.
  • the important "time lag” for bonded leaking steel is not involved, making it more accurate to capture the “time lag” and “temperature inversion” characteristics of leaking steel. Difficulties have affected the prediction accuracy of this method.
  • Patent CN102554171A discloses a continuous casting steel leakage prediction method, which introduces an adaptive algorithm into the BP neural network to achieve automatic optimization of the network structure, and proposes a frictional steel leakage prediction model based on logical judgment and neural network. Combining the accuracy of temperature monitoring with the sensitivity of friction monitoring, a prediction mechanism based on temperature monitoring and supplemented by friction monitoring has been established. The method is provided with four alarm points for temperature monitoring, single-couple, group-couple, friction logic judgment, and sequential network, and they have three levels of yellow, orange, and red. Each alarm point needs to set a corresponding threshold, and there are many forecast parameters. This makes the prediction method of steel leakage prediction poor in universality and mobility.
  • Patent CN108580827A discloses a method for predicting steel mold leakage based on condensed hierarchical clustering.
  • an equal amount of samples are randomly selected from each of the sample set of bonded steel leakage and normal working conditions.
  • online measured temperature samples constitute a random sample set, implement hierarchical clustering on the random sample set, and then check whether the online measured temperature samples belong to the bonded steel leakage clusters, in order to identify and predict steel leakage.
  • the invention gets rid of the dependence on the artificially defined parameters in the forecasting process, and only uses the characteristics of the bonding leaked steel and the normal operating temperature to determine whether the online measured temperature sample includes the leaked steel.
  • the limitation of this method is: 1) randomly select some samples in all steel leakage sample sets for clustering, and cannot take into account the common characteristics of all samples and the individual characteristics of a single sample, that is, the sample characteristics are not fully covered after clustering; 2) temperature The data is directly used for clustering after preprocessing, and the amount of data and calculation are large, which affects the real-time nature of online forecasting.
  • the present invention reduces the dimensionality and calculation amount of data through temperature feature extraction.
  • it uses all samples including steel leakage and normal working conditions for hierarchical clustering to take into account the common characteristics and personality characteristics of the samples. , On the premise of ensuring the accuracy of the forecast, improve the online computing speed and real-time.
  • the purpose of the present invention is to overcome the shortcomings of the existing forecasting method in the steel leakage feature extraction, and propose a crystallizer steel leakage forecasting method based on feature vectors and hierarchical clustering.
  • Historical data and temperature feature vectors of online measured data establish a feature vector library; normalize and hierarchical cluster the feature vector library; thereafter check and judge whether the feature vectors extracted online belong to the steel leakage cluster, and then identify and Predicting mold leakage.
  • a method for predicting steel mold leakage based on feature vector and hierarchical clustering includes the following steps:
  • each column of galvanic couples is used as a unit to extract the characteristics of the temperature change of the same column of galvanic couples along the casting direction when the steel leakage is bonded, including:
  • 2nd_Rising_V_Max the maximum value of the temperature rise rate of the second row
  • 1st_2nd_Time_Lag temperature rise time lag, that is, the time interval between the second row and the first row of thermocouple temperature starts to rise;
  • the second step is to extract the feature vector of normal working conditions
  • the third step is to extract online real-time temperature features
  • thermocouple Real-time collection and acquisition of the inner arc wide face, outer arc wide face, left narrow face, right narrow face copper plate rows and columns of the thermocouple at the current time and before M+N-1 seconds, a total of M+N seconds Temperature data
  • each column of galvanic couples is used as a unit to extract the online real-time temperature characteristics of the same column of galvanic couples along the casting direction.
  • the specific method is the same as the first step (2);
  • the fourth step is to establish a feature vector library
  • the feature vector sample library D is normalized to obtain the feature vector set S, and the normalized online measured temperature feature is recorded as s new .
  • the specific normalization method is as follows:
  • x ij is the value of the j-th feature of the i-th feature vector in the feature vector set S
  • x jmax and x jmin are the maximum and minimum values of the j-th feature of all feature vectors, respectively
  • represents the feature vector set The total number of vectors in S.
  • the fifth step feature vector hierarchical clustering
  • C pi is the i-th feature vector in cluster C p
  • C qj is the j-th feature vector in cluster C q
  • dist(C pi , C qj ) represents the Euclidean distance of feature vectors C pi and C qj
  • the cluster is classified as a steel breakout cluster C breakout , and the other cluster is recorded as a normal operating cluster C normal ; otherwise, steps (1) and (2) are re-executed until the steel leakage and normal
  • the clustering result of feature vector set composed of operating conditions and measured temperature meets the above judgment conditions;
  • the sixth step steel leakage identification and alarm
  • the above method for predicting steel leakage is suitable for the identification of steel leakage in continuous casting slabs such as slabs, square billets, round billets, and shaped billets.
  • a proposed method for predicting steel leakage based on feature vectors and hierarchical clustering avoids the cumbersome debugging and modification of parameters such as alarm thresholds, and overcomes the artificial dependence of previous steel leakage prediction methods It has good robustness and mobility; through temperature feature extraction, it can not only accurately identify the temperature pattern of bonded steel leakage, avoid false alarms and significantly reduce the number of false alarms, but also greatly reduce the amount of data calculation and operation time. Ensure the real-time nature of online forecasts.
  • Figure 1 is a schematic diagram of the distribution of four crystallizer copper plates and thermocouples
  • Figure 2 is a schematic diagram of the feature vector extraction of the temperature and the rate of change when the steel leakage is bonded;
  • Figure 3 is a schematic diagram of feature vector extraction of temperature and its rate of change under normal operating conditions
  • Figure 4 is a flowchart of hierarchical clustering of feature vector set and steel leakage recognition and alarm
  • Figure 5 is the online measured temperature 1
  • Fig. 6 is the results of hierarchical clustering and steel leakage prediction including online measured temperature feature vector 1;
  • Figure 7 is the online measured temperature 2
  • Figure 8 is the result of hierarchical clustering and steel leakage prediction including online measured temperature feature vector 2.
  • the invention is mainly composed of six parts: extracting the characteristic vector of the steel leakage, extracting the characteristic vector of the normal working condition, extracting the online real-time temperature characteristic vector, establishing the characteristic vector library, the hierarchical clustering of the characteristic vectors, and identifying and alarming the steel leakage.
  • Figure 1 is a schematic diagram of the distribution of four crystallizer copper plates and thermocouples.
  • the slab continuous casting mold is composed of four copper plates, including an outer arc wide surface copper plate, a left side narrow surface copper plate, an inner arc wide surface copper plate, and a right side narrow surface copper plate.
  • the length L is 900 mm, respectively.
  • thermocouples Two rows of measuring points are arranged on the horizontal cross-section of L1 210mm and L2 325mm, 19 rows of thermocouples are arranged in each row on the outer arc wide copper plate and the inner arc wide copper plate, the distance between the two galvanic couples L3 is 150mm, two There are 38 thermocouples on each of the wide-surface copper plates; a row of thermocouples are arranged on the center line on the left narrow-surface copper plate and a right-side narrow-surface copper plate, and two thermocouples are arranged on each of the two narrow-surface copper plates.
  • the total number of galvanic couples arranged by four copper plates is 80, and the distance from each galvanic couple to the hot surface of the copper plate of the crystallizer is equal.
  • Step 1 Extract the characteristic vector of cemented steel leakage
  • Fig. 2 is a schematic diagram of feature vector extraction of the temperature of the bonding leak steel and its temperature change rate. It can be seen from FIG. 2 that the feature vectors are extracted in units of each column of galvanic couples, and the characteristics of the temperature changes of the same column of galvanic couples along the casting direction when extracting the bonded leaked steel are extracted.
  • the specific features and extraction methods are as follows:
  • r ⁇ [1,2] represents the first and second rows of thermocouples respectively
  • T (r)i represents the temperature value of the r-th row thermocouple at time i
  • v (r)i represents the r- th row thermoelectric The value of the temperature change rate at time i.
  • T min the initial temperature when the temperature rises, and the corresponding time is recorded as t min .
  • 1st_Rising_Amplitude the first row of temperature rise amplitude, that is, the temperature at which the first row of temperature starts to rise and reaches the maximum, respectively, and calculate the difference between the two temperatures to obtain the temperature rise amplitude in °C:
  • 1st_Rising_V_Max the maximum value of the first row of temperature rise rate, that is, the maximum value of the temperature change rate of the first row of thermocouples obtained in the extraction step 2.1), in °C/s:
  • 1st_Falling_V_Ave the average value of the temperature drop rate of the first row, that is, the average value of the temperature rate of the temperature change rate of the first row thermocouple less than 0 obtained in the calculation step 2.1), the unit is °C/s;
  • 2nd_Rising_V_Max the maximum value of the temperature rise rate of the second row, that is, the maximum value of the temperature change rate of the second row thermocouple obtained in the extraction step 2.1), in °C/s;
  • Temperature rise time lag that is, mark the time when the temperature of the second row and the first row start to rise, and take the time interval of the two as the temperature rise time lag, the unit is s:
  • Step 2 Extract feature vectors under normal operating conditions
  • Figure 3 is a schematic diagram of feature vector extraction of temperature and its temperature change rate under normal operating conditions. It can be seen from FIG. 3 that the feature vectors are extracted in units of each column of galvanic couples to extract the characteristics of the temperature changes of the same column of galvanic couples along the casting direction under normal operating conditions. The features and their extraction methods are the same as in step one (2).
  • FIG. 4 is a flowchart of feature vector set hierarchical clustering and steel leakage recognition and determination. It can be seen from the figure that the hierarchical clustering of feature vector sets and the identification and determination of steel leakage mainly include the following steps:
  • Step 3 Extract online real-time temperature feature vector
  • the feature vectors are extracted in units of each column of galvanic couples, and the characteristics of the temperature changes of the same column of galvanic couples along the casting direction during online measurement are extracted.
  • the features and their extraction methods are the same as in step one (2).
  • Step four establish a feature vector library
  • x ij is the value of the j-th feature of the i-th feature vector in the feature vector set S
  • x jmax and x jmin are the maximum and minimum values of the j-th feature of all 61 feature vectors, respectively.
  • Step 5 Hierarchical clustering of feature vectors
  • C pi is the i-th feature vector in cluster C p
  • C qj is the j-th feature vector in cluster C q
  • dist(C pi , C qj ) represents the Euclidean distance of feature vectors C pi and C qj
  • the cluster is classified as a steel breakout cluster C breakout , and the other cluster is recorded as a normal operating cluster C normal ; otherwise, steps (1) and (2) are re-executed until the steel leakage and normal
  • the clustering result of feature vector set composed of operating conditions and measured temperature meets the above judgment conditions;
  • Fig. 5 shows the temperature diagrams of the first and second rows of galvanic couples measured online at temperature 1.
  • the vertical line on the right side of the figure shows the current time when online detection is performed, and the 24 seconds before this time, a total of 25 seconds of temperature data.
  • the feature vector obtained after feature extraction for online temperature 1 is:
  • Fig. 6 is a hierarchical clustering of the feature vector set containing online measured temperature feature vectors, that is, a feature vector set containing s new and a prediction result of steel leakage. It can be seen from the figure that after normalization and hierarchical clustering, the feature vector set is clustered into two clusters: the cluster on the left contains all the bonded leaking steel feature vectors labeled 1 and 5 labels 2 Normal working condition samples, the ratio of the characteristic vector of bonded steel leakage in this type of cluster is greater than 90% of the total number of samples of bonded steel leakage, the ratio of the characteristic vector of normal working conditions is less than 20% of the total number of samples of the normal working condition, that is, 6
  • the feature vector meets the criteria for cluster classification, and the hierarchical clustering of the feature vector set is successful, so this cluster is recorded as a steel leakage cluster C breakout ; another cluster with a large number of 2 is recorded as a normal working cluster C normal .
  • the online measured temperature 1 feature vector s new obtained by feature extraction that is, the sample labeled "N”
  • belongs to the normal working condition cluster C normal after clustering and does not belong to the bonding leak Steel cluster C breakout , so it is judged as normal working condition, then continue to update the temperature sequence, perform steps three, four, five, six.
  • Fig. 7 shows the temperature diagrams of the first and second row galvanic couples of the temperature 2 measured online.
  • the vertical line on the right side of the figure shows the current time when online detection is performed, and the 24 seconds before this time, a total of 25 seconds of temperature data.
  • the feature vector obtained after feature extraction for online temperature 2 is:
  • Fig. 8 is a graph containing hierarchical clustering and steel leakage prediction results of the online measured temperature feature vector 2, that is, the feature vector set including s new . It can be seen from the figure that after normalization and hierarchical clustering, the feature vector set is clustered into two clusters: the cluster on the left contains all the bonded leaked steel samples labeled 1 and 5 labeled 2 In normal working condition samples, the percentage of the characteristic vector of bonded steel leakage in this cluster is greater than 90% of the total number of samples of the steel leakage, and the ratio of the characteristic vector of normal operating conditions is less than 20% of the total number of samples in the normal working condition, that is, 6 features
  • the vector meets the criteria for cluster determination, and the hierarchical clustering of the feature vector set is successful, so this cluster is recorded as a steel leakage cluster C breakout ; then another cluster with a large number of 2 is recorded as a normal operating cluster C normal .

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Abstract

一种基于特征向量和层次聚类的结晶器漏钢预报方法，该预报方法分别提取黏结漏钢、正常工况历史数据以及在线实测数据的温度特征向量，建立特征向量样本集；对样本集进行归一化处理，并进行层次聚类；此后检查和判断在线提取的特征向量是否从属于漏钢类簇，进而识别和预报结晶器漏钢。上述预报方法回避了报警阈值等参数繁琐的调试和修改环节，克服了以往漏钢预报方法的人为依赖性，具有良好的鲁棒性和迁移性；通过温度特征提取，不仅可准确识别黏结漏钢温度模式，避免漏报并显著降低了误报次数，还能够极大压缩数据计算量和运算时间，确保在线预报的实时性。

Description

一种基于特征向量和层次聚类的结晶器漏钢预报方法 技术领域
本发明属于钢铁冶金连铸检测技术领域，涉及一种基于特征向量和层次聚类的结晶器漏钢预报方法。
背景技术
黏结漏钢是指连铸过程中弯月面附近较薄的初生坯壳发生破裂，钢液渗出后与结晶器铜板接触发生黏结，随结晶器振动和铸坯下移，黏结反复撕裂-愈合且不断下移，当其移出结晶器出口后失去了铜板的支撑约束，钢液溢出，造成漏钢。漏钢不仅危及现场操作人员的安全，严重损坏连铸设备，同时将导致连铸生产被迫中断，设备维修和生产成本大幅上升。因此，针对漏钢的在线监控和预报始终是连铸过程控制的重中之重，对保障生产顺利进行具有重要意义。
目前，现有的漏钢预报方法主要通过在结晶器铜板上嵌入安装测温热电偶，根据热电偶温度的变化来监控和判别铸坯与铜板之间是否发生黏结。实践表明，黏结发生时热电偶温度随时间的变化率及其幅值均与正常工况存在明显的差异，因此，可通过提取和归纳热电偶温度及其速率、幅值的共性特征，结合逻辑判断或神经网络方法，区分和识别黏结的典型温度特征，在线预报结晶器漏钢。
从现有漏钢预报技术的实际应用情况来看，基于逻辑判断的漏钢预报方法对连铸设备、工艺参数和物性参数的依赖性较强，当工艺调整和拉速提升时，阈值变动大，导致误报率和漏报率大幅上升；神经网络方法对学习和训练样本的要求较高，样本不全或无效时都会严重影响其预报效果，模型的迁移能力较低。实践中为尽力避免漏报，在设计预报算法或设置报警阈值时都会预留出一定余赋，在工艺调整、设备维护更换等情况下，预报阈值需人为频繁调试和修 正，若设置不当或调整不及时，误报也很难准确识别和剔除，甚至导致漏报，是目前漏钢预报系统面临的常见问题。
专利CN106980729A公开了一种基于混合模型的连铸漏钢预报方法，该方法采集和存储现场所有热电偶温度的实时数据，判断每个热电偶温度在时间序列上的变化是否符合黏结漏钢时的温度变化波形，保存判断结果为Y(i,j,t)，如果Y(i,j,t)在设定的阈值范围内则标记为热电偶异常，并统计当前热电偶所在行和上一行异常热电偶的数目，比较输出的异常热电偶总数与黏结警告和黏结报警的热电偶数目阈值。该方法侧重单支热电偶的温度变化和异常电偶的叠加数目，对黏结漏钢重要的“时滞性”并没有涉及，使得准确捕捉漏钢的“时滞”和“温度倒置”特征较为困难，影响了该方法的预报精度。
专利CN102554171A公开了一种连铸漏钢预报方法，将自适应算法引入BP神经网络，以实现网络结构的自动优化，并提出了一种基于逻辑判断和神经网络的摩擦力监控漏钢预报模型，有机结合温度监控的准确性与摩擦力监控的灵敏性，建立了以温度监控为主、摩擦力监控为辅的预报机制。该方法设有温度监控单偶、组偶以及摩擦力逻辑判断、时序网络四处报警点，且均有黄、橙、红三种级别，每处报警点均需设置相应的阈值，预报参数众多，使得该方法漏钢预报的普适性和迁移性较差。
专利CN108580827A公开了一种基于凝聚层次聚类预报结晶器漏钢的方法，通过建立黏结漏钢和正常工况样本库，从黏结漏钢样本集和正常工况样本集中各自随机取选取等量样本，与在线实测温度样本构成随机样本集，对该随机样本集实施层次聚类，之后，检测在线实测温度样本是否属于黏结漏钢类簇，以此识别和预报漏钢。该发明摆脱了在预报过程中对人为定义参数的依赖性，仅利用黏结漏钢和正常工况温度的特征判断在线实测温度样本是否包含属于漏 钢。但该方法的局限在于：1)在所有漏钢样本集中随机选择部分样本进行聚类，无法兼顾全部样本的共性特征和单个样本的个性特征，即聚类后样本特征覆盖不全面；2)温度数据经预处理后直接用于聚类，数据量和计算量均较大，影响在线预报的实时性。针对上述问题，本发明一方面通过温度特征提取，降低数据的维度和运算量，另一方面，采用包括漏钢、正常工况的全体样本进行层次聚类，以兼顾样本的共性特征和个性特征，在确保预报准确性的前提下，提升在线运算速度和实时性。
发明内容
本发明的目的是为了克服现有预报方法在漏钢特征提取方面存在的不足，提出一种基于特征向量和层次聚类的结晶器漏钢预报方法，通过提取全部黏结漏钢样本、正常工况历史数据以及在线实测数据的温度特征向量，建立特征向量库；对特征向量库进行归一化处理和层次聚类；此后检查和判断在线提取的特征向量是否从属于漏钢类簇，进而识别和预报结晶器漏钢。
为达到上述目的，本发明的技术方案如下：
一种基于特征向量和层次聚类的结晶器漏钢预报方法，包括以下步骤：
第一步，提取黏结漏钢特征向量
(1)获取黏结漏钢历史温度数据：标记黏结位置所在电偶列第一排电偶温度最高时刻，并选取其前M秒、后N-1秒共计M+N秒的温度数据；
(2)提取和构建第一排、第二排电偶温度特征向量：分别以每列电偶为单位，提取黏结漏钢时同列电偶温度沿浇铸方向的变化特征，具体包括：
1st_Rising_Amplitude：第一排温度上升幅值；
1st_Rising_V_Max：第一排温度上升速率的最大值；
1st_Falling_V_Ave：第一排温度下降速率均值；
2nd_Rising_V_Max：第二排温度上升速率的最大值；
1st_2nd_Time_Lag：温度上升时滞，即第二排与第一排热电偶温度开始上升时刻的时间间隔；
由此构建特征向量：
s＝[1st_Rising_Amplitude,1st_Rising_V_Max,1st_Falling_V_Ave,2nd_Rising_V_Max,1st_2nd_Time_Lag]
第二步，提取正常工况特征向量
(1)获取正常工况历史温度数据：任意截取连续M+N秒的温度数据；提取和构建第一排、第二排电偶温度特征向量：分别以每列电偶为单位，提取正常工况下同列电偶温度沿浇铸方向的变化特征，具体方法同第一步步骤(2)；
第三步，提取在线实时温度特征
(1)实时采集和获取结晶器内弧宽面、外弧宽面、左侧窄面、右侧窄面铜板各行、列热电偶当前时刻及之前M+N-1秒，共计M+N秒的温度数据；
(2)提取和构建第一排、第二排电偶温度特征向量：分别以每列电偶为单位，提取在线实时温度同列电偶温度沿浇铸方向的变化特征，具体方法同第一步步骤(2)；
第四步，建立特征向量库
(1)依据第一、第二、第三步提取的黏结漏钢、正常工况与在线实测的温度特征建立特征向量样本库D；
(2)对特征向量样本库D作归一化处理，得到特征向量集S，将归一化后的在线实测温度特征记为s new，具体归一化方法如下：
Figure PCTCN2019100130-appb-000001
其中，x ij为特征向量集S中第i个特征向量第j维特征的值，x jmax、x jmin分别为所有特征向量第j维特征的最大值和最小值，|S|表示特征向量集S中向量的总 数。
第五步，特征向量层次聚类
(1)对第四步得到的特征向量集S实施层次聚类，具体过程包括：
1.1)将特征向量集S中的每个向量s看作一个初始类簇C i＝{s i}，建立类簇集合C＝{C 1,C 2,...,C k}；其中s i表示S中第i个向量，C i表示第i个类簇，i＝1,2,...,k，k表示特征向量集S中向量的总数；
1.2)计算和确定类簇集合C中任意两个类簇C p、C q的距离：
d(C p,C q)＝min(dist(C pi,C qj))
其中，C pi为类簇C p中第i个特征向量，C qj为类簇C q中第j个特征向量，dist(C pi,C qj)表示特征向量C pi、C qj的欧氏距离；计算类簇C p、C q中任意两个向量的距离，取距离的最小值min作为类簇C p和C q的距离。
1.3)标记经步骤1.2)计算后距离最小的两个类簇C m和C n，将C m和C n合并成为一个新的类簇C {m,n}并加入集合C，同时删除原有的类簇C m和C n，经过类簇的添加和删除后，此时集合C中类簇的总数目减1；
1.4)循环执行步骤1.2)～1.3)，当类簇集合C中仅剩两个类簇时，结束循环，完成聚类过程；
(2)检查聚类结果是否满足以下判定条件，即：
所有黏结漏钢特征向量的90％以上属于同一个类簇，且该类簇中正常工况特征向量所占的比率低于20％；
满足此条件则将该类簇记为漏钢类簇C breakout，另一个类簇记为正常工况类簇C normal；否则，重新执行步骤(1)、(2)，直至由漏钢、正常工况以及实测温度构成的特征向量集聚类结果满足上述判定条件为止；
第六步，漏钢识别与报警
判断新特征向量s new是否属于类簇C breakout，如果是，则发出漏钢警报；否则，继续执行第三、四、五、六步。
上述预报漏钢的方法适用于板坯、方坯、圆坯、异型坯等连铸坯漏钢识别。
本发明的有益效果是：所提出的一种基于特征向量和层次聚类的结晶器漏钢预报方法，回避了报警阈值等参数繁琐的调试和修改环节，克服了以往漏钢预报方法的人为依赖性，具有良好的鲁棒性和迁移性；通过温度特征提取，不仅可准确识别黏结漏钢温度模式，避免漏报并显著降低了误报次数，还极大压缩了数据计算量和运算时间，确保在线预报的实时性。
附图说明
图1是四张结晶器铜板与热电偶分布示意图；
图2是黏结漏钢时温度及其变化率的特征向量提取示意图；
图3是正常工况下温度及其变化率的特征向量提取示意图；
图4是特征向量集层次聚类及漏钢识别与报警流程图；
图5是在线实测温度1；
图6是包含在线实测温度特征向量1的层次聚类及漏钢预报结果；
图7是在线实测温度2；
图8是包含在线实测温度特征向量2的层次聚类及漏钢预报结果。
具体实施方式
下面通过具体的实施例，并结合附图对本发明做进一步的阐述。
本发明主要由六个部分构成：提取黏结漏钢特征向量、提取正常工况特征向量、提取在线实时温度特征向量、建立特征向量库、特征向量层次聚类、漏钢识别与报警。
图1是四张结晶器铜板与热电偶分布示意图。板坯连铸结晶器由四张铜板 构成，包括外弧宽面铜板、左侧窄面铜板、内弧宽面铜板、右侧窄面铜板长度L为900mm，分别在四张铜板距结晶器上口L1为210mm、L2为325mm的水平横截面上布置2行测点，外弧宽面铜板和内弧宽面铜板上每行布置19列热电偶，两个电偶间距L3为150mm，两张宽面铜板各布置38支热电偶；左侧窄面铜板和右侧窄面铜板位于中心线各布置一列热电偶，两张窄面铜板各布置2支热电偶。四张铜板布置电偶总数共计80支，每支电偶至结晶器铜板热面距离相等。
步骤一、提取黏结漏钢特征向量
(1)获取黏结漏钢历史温度数据：标记黏结位置所在电偶列第一排电偶温度最高时刻，并选取其前15秒、后9秒共计25秒的温度数据；
对于黏结漏钢历史温度，选取30例温度样本。
(2)提取和构建第一排、第二排电偶温度特征向量
图2为黏结漏钢温度及其温度变化率的特征向量提取示意图。由图2可知，特征向量的提取分别以每列电偶为单位，提取黏结漏钢时同列电偶温度沿浇铸方向的变化特征，具体特征及其提取方法如下：
2.1)计算同一测点处温度数据在5秒内的变化率，即：
Figure PCTCN2019100130-appb-000002
式中，r∈[1,2]分别表示第一排、第二排热电偶，T (r)i表示第r排热电偶第i时刻的温度数值，v (r)i表示第r排热电偶第i时刻温度变化率的数值。
2.2)确定温度上升时的起始温度及其对应的时刻：
首先，获取包含当前时刻及之前共计25秒内温度的最大值T max及其对应的时刻t max，即：
T max＝max(T i),i＝1,2,...,25，
然后，从T max前向遍历，获取其之前温度的最小值T min及时刻t min，即：
T min＝min(T i),i＝1,2,...,t max
则取T min为温度上升时的起始温度，其对应的时刻记为t min。
2.3)提取相应的特征构建特征向量：
1st_Rising_Amplitude：第一排温度上升幅值，即分别标记第一排温度开始上升及达到最大时的温度，计算两温度间的差值，得到温度上升幅值，单位为℃：
1st_Rising_Amplitude＝T (1)max-T (1)min
1st_Rising_V_Max：第一排温度上升速率的最大值，即提取步骤2.1)所得第一排热电偶温度变化率的最大值，单位为℃/s：
1st_Rising_V_Max＝max(v (1))
1st_Falling_V_Ave：第一排温度下降速率均值，即计算步骤2.1)所得第一排热电偶温度变化率小于0的温度速率的均值，单位为℃/s；
Figure PCTCN2019100130-appb-000003
2nd_Rising_V_Max：第二排温度上升速率的最大值，即提取步骤2.1)所得第二排热电偶温度变化率的最大值，单位为℃/s；
2nd_Rising_V_Max＝max(v (2))
1st_2nd_Time_Lag：温度上升时滞，即分别标记第二排与第一排温度开始上升时对应的时刻，取二者的时间间隔作为温度上升时滞，单位为s：
1st_2nd_Time_Lag＝t (2)min-t (1)min
由此构建特征向量：
s＝[1st_Rising_Amplitude,1st_Rising_V_Max,1st_Falling_V_Ave,2nd_Rising_V_Max,1st_2nd_Time_Lag]
步骤二、提取正常工况特征向量
(1)获取正常工况历史温度数据：任意截取连续25秒的温度数据；
(2)提取和构建第一排、第二排电偶温度特征向量
图3为正常工况下温度及其温度变化率的特征向量提取示意图。由图3可知，特征向量的提取分别以每列电偶为单位，提取正常工况下同列电偶温度沿浇铸方向的变化特征，特征及其提取方法与步骤一(2)相同。
对于正常工况温度，选取30例温度样本。
图4是特征向量集层次聚类和漏钢识别与判定流程图。从图中可以看出，特征向量集层次聚类和漏钢识别与判定主要包含以下步骤：
步骤三、提取在线实时温度特征向量
(1)实时采集和获取结晶器内弧宽面、外弧宽面、左侧窄面、右侧窄面铜板各行、列热电偶当前时刻及之前24秒，共计25秒的温度数据；
(2)提取和构建第一排、第二排电偶温度特征向量：
特征向量的提取分别以每列电偶为单位，提取在线实测时同列电偶温度沿浇铸方向的变化特征，特征及其提取方法与步骤一(2)相同。
步骤四、建立特征向量库
(1)依据步骤一、步骤二、步骤三提取的黏结漏钢、正常工况与在线实测的温度特征建立特征向量样本库D，共计61例；
(2)对特征向量样本库D作归一化处理，得到特征向量集S，并将归一化后的在线实测温度特征向量记为s new。具体归一化方法如下：
Figure PCTCN2019100130-appb-000004
其中，x ij为特征向量集S中第i个特征向量第j维特征的值，x jmax、x jmin分别为所有61特征向量第j维特征的最大值和最小值。
步骤五，特征向量层次聚类
(1)对步骤四得到的特征向量集S实施层次聚类；
1.1)将特征向量集S中的每个向量s看作一个初始类簇C i＝{s i}，建立类簇集 合C＝{C 1,C 2,...,C 81}；其中s i表示S中第i个向量，C i表示第i个类簇，i＝1,2,…,61。
1.2)计算和确定类簇集合C中任意两个类簇C p、C q的距离：
d(C p,C q)＝min(dist(C pi,C qj))
其中，C pi为类簇C p中第i个特征向量，C qj为类簇C q中第j个特征向量，dist(C pi,C qj)表示特征向量C pi、C qj的欧氏距离；计算类簇C p、C q中任意两个向量的距离，取距离的最小值min作为类簇C p和C q的距离。
1.3)标记经步骤1.2)计算后距离最小的两个类簇C m和C n，将C m和C n合并成为一个新的类簇C {m,n}并加入集合C，同时删除原有的类簇C m和C n，经过类簇的添加和删除后，此时集合C中类簇的总数目减1；
1.4)循环执行步骤1.2)～1.3)，当类簇集合C中仅剩两个类簇时，结束循环，完成聚类过程；
(2)检查聚类结果是否满足以下判定条件，即：
所有黏结漏钢特征向量的90％以上属于同一个类簇，且该类簇中正常工况特征向量所占的比率低于20％；
满足此条件则将该类簇记为漏钢类簇C breakout，另一个类簇记为正常工况类簇C normal；否则，重新执行步骤(1)、(2)，直至由漏钢、正常工况以及实测温度构成的特征向量集聚类结果满足上述判定条件为止；
步骤六，漏钢识别与报警
判断新特征向量s new是否属于类簇C breakout，如果是，则发出漏钢警报；否则，继续执行步骤三、四、五、六。
图5表示在线实测温度1的第一、二排电偶温度图。图中右侧垂直线表示在线检测时的当前时刻，连同该时刻之前的24个时刻，共计25秒的温度数据。对在线温度1进行特征提取后得到的特征向量为：
s new＝[0.4,0.18,-1.18,1.72,0]，
图6是包含在线实测温度特征向量1，即包含s new的特征向量集层次聚类及漏钢预报结果图。从图中可以看出，经归一化和层次聚类后，特征向量集聚类为两个类簇：左侧的类簇包含了所有标号为1的黏结漏钢特征向量和5个标号为2的正常工况样本，该类簇中黏结漏钢特征向量所占比率大于黏结漏钢样本总数的90％，正常工况特征向量所占比率小于正常工况样本总数的20％，即6个特征向量，符合类簇判定条件，特征向量集层次聚类成功，所以将该类簇记为漏钢类簇C breakout；则另一个包含标号2较多的类簇记为正常工况类簇C normal。从图6中亦可以看出，在线实测温度1经特征提取得到的特征向量s new，即标号为“N”的样本，在聚类后属于正常工况类簇C normal，并不属于黏结漏钢类簇C breakout，因此判断为正常工况，则继续更新温度序列，执行步骤三、四、五、六。
图7表示在线实测温度2的第一、二排电偶温度图。图中右侧垂直线表示在线检测时的当前时刻，连同该时刻之前的24个时刻，共计25秒的温度数据。对在线温度2进行特征提取后得到的特征向量为：
s new＝[7.4,1.06,-0.29,1.36,12]，
图8是包含在线实测温度特征向量2，即包含s new的特征向量集层次聚类及漏钢预报结果图。从图中可以看出，经归一化和层次聚类后，特征向量集聚类为两个类簇：左侧的类簇包含了所有标号为1的黏结漏钢样本和5个标号为2的正常工况样本，该类簇中黏结漏钢特征向量所占比率大于黏结漏钢样本总数的90％，正常工况特征向量所占比率小于正常工况样本总数的20％，即6个特征向量，符合类簇判定条件，特征向量集层次聚类成功，所以将该类簇记为漏钢类簇C breakout；则另一个包含标号2较多的类簇记为正常工况类簇C normal。从图8中亦可以看出，在线实测温度2经特征提取得到的特征向量s new，即标号为“B” 的样本，在聚类后属于黏结漏钢类簇C breakout，所以判定为漏钢，发出漏钢警报。
以上所述实施例仅表达本发明的实施方式，但并不能因此而理解为对本发明专利的范围的限制，应当指出，对于本领域的技术人员来说，在不脱离本发明构思的前提下，还可以做出若干变形和改进，这些均属于本发明的保护范围。

Claims (2)

  1. 一种基于特征向量和层次聚类的结晶器漏钢预报方法，其特征在于，该预报方法分别提取黏结漏钢、正常工况历史数据以及在线实测数据的温度特征向量，建立特征向量样本集；对样本集进行归一化处理，并进行层次聚类；此后检查和判断在线提取的特征向量是否从属于漏钢类簇，进而识别和预报结晶器漏钢，包括以下步骤：
    第一步，提取黏结漏钢特征向量
    (1)获取黏结漏钢历史温度数据：标记黏结位置所在电偶列第一排电偶温度最高时刻，并选取其前M秒、后N-1秒共计M+N秒的温度数据；
    (2)提取和构建第一排、第二排电偶温度特征向量；
    第二步，提取正常工况特征向量
    (1)获取正常工况历史温度数据：任意截取连续M+N秒的温度数据；
    (2)提取和构建第一排、第二排电偶温度特征向量；
    第三步，提取在线实时温度特征
    (1)实时采集和获取结晶器内弧宽面、外弧宽面、左侧窄面、右侧窄面铜板各行、列热电偶当前时刻及之前M+N-1秒，共计M+N秒的温度数据；
    (2)提取和构建第一排、第二排电偶温度特征向量；
    第四步、建立特征向量库
    (1)依据第一、第二、第三步提取的黏结漏钢、正常工况与在线实测的温度特征建立特征向量样本库D；
    (2)对特征向量样本库D作归一化处理，得到特征向量集S，将归一化后的在线实测温度特征记为s new；
    所述的特征向量的归一化方法如下：
    Figure PCTCN2019100130-appb-100001
    其中，x ij为特征向量集S中第i个特征向量第j维特征的值，x jmax、x jmin分别为所有特征向量第j维特征的最大值和最小值，|S|表示特征向量集S中向量的总数；
    第五步，特征向量层次聚类
    (1)对第四步得到的特征向量集S实施层次聚类，具体过程包括：
    1.1)将特征向量集S中的每个向量s看作一个初始类簇C i＝{s i}，建立类簇集合C＝{C 1,C 2,...,C k}；其中s i表示S中第i个向量，C i表示第i个类簇，i＝1,2,...,k，k表示特征向量集S中向量的总数；
    1.2)计算和确定类簇集合C中任意两个类簇C p、C q的距离：
    d(C p,C q)＝min(dist(C pi,C qj))
    其中，C pi为类簇C p中第i个特征向量，C qj为类簇C q中第j个特征向量，dist(C pi,C qj)表示特征向量C pi、C qj的欧氏距离；计算类簇C p、C q中任意两个向量的距离，取距离的最小值min作为类簇C p和C q的距离；
    1.3)标记经步骤3.2)计算后距离最小的两个类簇C m和C n，将C m和C n合并成为一个新的类簇C {m,n}并加入集合C，同时删除原有的类簇C m和C n，经过类簇的添加和删除后，此时集合C中类簇的总数目减1；
    1.4)循环执行步骤3.2)～3.3)，当类簇集合C中仅剩两个类簇时，结束循环，完成聚类过程；
    (2)检查聚类结果是否满足以下判定条件，即：
    所有黏结漏钢特征向量的90％以上属于同一个类簇，且该类簇中正常工况特征向量所占的比率低于20％；
    满足此条件则将该类簇记为漏钢类簇C breakout，另一个类簇记为正常工况类簇C normal；否则，重新执行步骤(1)、(2)，直至由漏钢、正常工况以及实测温 度构成的特征向量集聚类结果满足上述判定条件为止；
    第六步，漏钢识别与报警
    判断新特征向量s new是否属于类簇C breakout，如果是，则发出漏钢警报；否则，继续执行第三、四、五、六步；
    所述的第一步(2)、第二步(2)及第三步(2)涉及的温度特征提取方法相同，分别以每列电偶为单位，提取不同工况下同列电偶温度沿浇铸方向的变化特征，具体包括：
    1st_Rising_Amplitude：第一排温度上升幅值；
    1st_Rising_V_Max：第一排温度上升速率的最大值；
    1st_Falling_V_Ave：第一排温度下降速率均值；
    2nd_Rising_V_Max：第二排温度上升速率的最大值；
    1st_2nd_Time_Lag：温度上升时滞，即第二排与第一排热电偶温度开始上升时刻的时间间隔；
    由此构建特征向量：
    s＝[1st_Rising_Amplitude,1st_Rising_V_Max,1st_Falling_V_Ave,2nd_Rising_V_Max,1st_2nd_Time_Lag]。
  2. 根据权利要求1所述的一种基于特征向量和层次聚类的结晶器漏钢预报方法，其特征在于，所述预报漏钢的方法适用于板坯、方坯、圆坯、异型坯连铸过程的漏钢在线预报。
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