基于慢特征分析的分布式動態工業過程運行狀態評價

鐘林生; 常玉清; 王福利; 高世紅

doi:10.16383/j.aas.c230154

基于慢特征分析的分布式動態工業過程運行狀態評價

doi: 10.16383/j.aas.c230154

鐘林生^1,,
常玉清^1,,
王福利^{1, 2,},
高世紅^3,

1.
東北大學信息科學與工程學院沈陽 110819
2.
東北大學流程工業綜合自動化國家重點實驗室沈陽 110819
3.
山西大學自動化與軟件學院太原 030006

基金項目: 國家自然科學基金(62273078, 61973057), 國家重點研發計劃(2021YFF0602404, 2021YFC2902703)資助

詳細信息

作者簡介:
鐘林生：東北大學信息科學與工程學院博士研究生. 主要研究方向為復雜工業過程運行狀態評價, 機器學習. E-mail: zhonglinsheng_neu@163.com

常玉清：東北大學信息科學與工程學院教授. 主要研究方向為復雜工業過程運行狀態評價, 故障診斷. 本文通信作者. E-mail: changyuqing@ise.neu.edu.cn

王福利：東北大學信息科學與工程學院教授. 主要研究方向為復雜工業過程智能控制, 故障診斷和運行狀態評價. E-mail: wangfuli@ise.neu.edu.cn

高世紅：山西大學自動化與軟件學院講師. 主要研究方向為航天器姿態控制, 有限時間控制和預設性能控制. E-mail: gaoshihong@sxu.edu.cn

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- 文章訪問數: 230
- HTML全文瀏覽量: 60
- PDF下載量: 106
- 被引次數: 0
出版歷程
- 收稿日期: 2023-03-26
- 錄用日期: 2023-11-19
- 網絡出版日期: 2024-02-19
- 刊出日期: 2024-04-26

Distributed Operating Performance Assessment of Dynamic Industrial Processes Based on Slow Feature Analysis

1.
College of Information Science and Engineering, Northeastern University, Shenyang 110819
2.
State Key Laboratory of Synthetical Automation for Process Industries, Northeastern University, Shenyang 110819
3.
School of Automation and Software Engineering, Shanxi University, Taiyuan 030006

Funds: Supported by National Natural Science Foundation of China (62273078, 61973057) and National Key Research and Development Program of China (2021YFF0602404, 2021YFC2902703)

More Information

Author Bio:
ZHONG Lin-Sheng　Ph.D. candidate at the College of Information Sci-ence and Engineering, Northeastern University. His research interest covers complex process operating performance assessment and machine learning

CHANG Yu-Qing　Professor at the College of Information Science and Engineering, Northeastern University. Her research interest covers complex process operating performance assessment and fault diagnosis. Corresponding author of this paper

WANG Fu-Li　Professor at the College of Information Science and Engineering, Northeastern Univer-sity. His research interest covers complex process intelligent control, fault diagnosis, and operating performance assessment

GAO Shi-Hong　Lecturer at the School of Automation and Software Engineering, Shanxi University. Her research interest covers spacecraft attitude control, finite-time control, and prescribed performance control

摘要

摘要: 現代工業過程通常具有規模大、流程長和工序多的特點, 導致傳統的集中式建模方法會淹沒過程的局部變化信息, 從而無法及時識別早期的非優運行狀態. 此外, 閉環控制的廣泛應用使得過程變量普遍存在時序相關性. 針對以上問題, 提出一種基于慢特征分析(Slow feature analysis, SFA)的分布式動態工業過程運行狀態評價方法. 首先, 結合動態時間規整(Dynamic time warping, DTW)和K-medoids聚類算法對過程進行分解; 然后, 對每一變量子塊建立相應的動態慢特征分析(Dynamic slow feature analysis, DSFA)模型; 最后, 利用貝葉斯推理獲得全局的綜合評價指標. 通過在數值案例和金濕法冶金過程的仿真應用, 驗證了該方法的有效性.
- 分布式模型 /
- 運行狀態評價 /
- 慢特征分析 /
- 動態時間規整 /
- K-medoids聚類
Abstract: The modern industrial processes are generally characterized by large scale, long processes and multiple procedures. In this case, the traditional centralized model may submerge the local change information of the processes, thus failing to identify the early non-optimal operation status in time. In addition, the wide application of closed-loop control brings the universal existence of temporal correlations of process variables. In view of the above problem, a distributed operating performance assessment scheme of dynamic industrial processes based on slow feature analysis (SFA) is proposed. First, the process decomposition is realized by combining dynamic time warping (DTW) and K-medoids clustering algorithms. Second, the corresponding dynamic slow feature analysis (DSFA) model is established for each sub-block. Finally, the overall comprehensive assessment index is obtained through Bayesian inference. The effectiveness of the scheme is verified by numerical examples and gold hydrometallurgy process.
- Distributed model /
- operating performance assessment /
- slow feature analysis (SFA) /
- dynamic time warping (DTW) /
- K-medoids clustering

HTML全文

圖 1 基于DDSFA的運行狀態評價流程圖

Fig. 1 Flow diagram of DDSFA-based operating performance assessment

下載: 全尺寸圖片幻燈片

圖 2 數值仿真算例中, 案例1的運行狀態評價結果

Fig. 2 The operating performance assessment result of case 1 in the numerical example

下載: 全尺寸圖片幻燈片

圖 3 數值仿真算例中, 案例2的運行狀態評價結果

Fig. 3 The operating performance assessment result of case 2 in the numerical example

下載: 全尺寸圖片幻燈片

圖 4 金濕法冶金過程工藝流程圖

Fig. 4 The flow chart of gold hydrometallurgy process

下載: 全尺寸圖片幻燈片

圖 5 金濕法冶金過程中, 案例3的運行狀態評價結果

Fig. 5 The operating performance assessment result of case 3 in gold hydrometallurgy process

下載: 全尺寸圖片幻燈片

圖 6 金濕法冶金過程中, 案例4的運行狀態評價結果

Fig. 6 The operating performance assessment result of case 4 in gold hydrometallurgy process

下載: 全尺寸圖片幻燈片

表 1 不同算法在數值仿真算例中的漏報率(%)

Table 1 Missed alarm rates of different methods inthe numerical example (%)

方法	DPCA	DDPCA	DSFA	DDSFA
案例1	20.25	4.25	19.25	7.00
案例2	94.00	90.50	24.00	18.25

下載: 導出CSV

表 2 金濕法冶金過程的變量

Table 2 The variables of gold hydrometallurgy process

序號	子工序	變量名稱
1	一次氰化浸出	礦漿濃度
2		入口礦漿流量
3		浸出槽1的${\rm{NaCN}}$流量
4		浸出槽2的${\rm{NaCN}}$流量
5		浸出槽4的${\rm{NaCN}}$流量
6		空氣流量
7		浸出槽溶解氧濃度
8		浸出槽1的${\rm{CN}}^-$濃度
9		浸出槽2的${\rm{CN}}^-$濃度
10		浸出槽4的${\rm{CN}}^-$濃度
11	一次洗滌	立式壓濾機進料壓力
12		立式壓濾機液壓壓力
13		立式壓濾機擠壓壓力
14	二次氰化浸出	礦漿濃度
15		入口礦漿流量
16		浸出槽1的${\rm{NaCN}}$流量
17		浸出槽2的${\rm{NaCN}}$流量
18		浸出槽4的${\rm{NaCN}}$流量
19		空氣流量
20		浸出槽溶解氧濃度
21		浸出槽1的${\rm{CN}}^-$濃度
22		浸出槽2的${\rm{CN}}^-$濃度
23		浸出槽4的${\rm{CN}}^-$濃度
24	二次洗滌	立式壓濾機進料壓力
25		立式壓濾機液壓壓力
26		立式壓濾機擠壓壓力
27	置換	脫氧塔真空度
28		貴液中的$ {\left[ {{\rm{Au}}{{\left( {{\rm{CN}}} \right)}_2}} \right]^ - }$濃度
29		貧液中的$ {\left[ {{\rm{Au}}{{\left( {{\rm{CN}}} \right)}_2}} \right]^ - }$濃度
30		鋅粉添加速度

下載: 導出CSV

表 3 金濕法冶金過程變量的子塊劃分結果

Table 3 Sub-block division result of process variables of gold hydrometallurgy

子塊	過程變量
1	27, 28, 29, 30
2	6, 7, 11, 12, 13
3	19, 20, 24, 25, 26
4	1, 2, 3, 4, 5, 8, 9, 10
5	14, 15, 16, 17, 18, 21, 22, 23

下載: 導出CSV

表 4 不同算法在金濕法冶金過程中的漏報率(%)

Table 4 Missed alarm rates of different methods in gold hydrometallurgy process (%)

方法	DPCA	DDPCA	DSFA	DDSFA
案例3	71.50	40.00	25.50	6.00
案例4	68.00	48.75	35.00	26.25

下載: 導出CSV

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