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              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

              鐘林生, 常玉清, 王福利, 高世紅. 基于慢特征分析的分布式動態工業過程運行狀態評價. 自動化學報, 2024, 50(4): 745?757 doi: 10.16383/j.aas.c230154
              引用本文: 鐘林生, 常玉清, 王福利, 高世紅. 基于慢特征分析的分布式動態工業過程運行狀態評價. 自動化學報, 2024, 50(4): 745?757 doi: 10.16383/j.aas.c230154
              Zhong Lin-Sheng, Chang Yu-Qing, Wang Fu-Li, Gao Shi-Hong. Distributed operating performance assessment of dynamic industrial processes based on slow feature analysis. Acta Automatica Sinica, 2024, 50(4): 745?757 doi: 10.16383/j.aas.c230154
              Citation: Zhong Lin-Sheng, Chang Yu-Qing, Wang Fu-Li, Gao Shi-Hong. Distributed operating performance assessment of dynamic industrial processes based on slow feature analysis. Acta Automatica Sinica, 2024, 50(4): 745?757 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
              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

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

              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: 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.
              • 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

                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

                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

                圖  4  金濕法冶金過程工藝流程圖

                Fig.  4  The flow chart of gold hydrometallurgy process

                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

                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

                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DPCADDPCADSFADDSFA
                案例120.254.2519.257.00
                案例294.0090.5024.0018.25
                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
                127, 28, 29, 30
                26, 7, 11, 12, 13
                319, 20, 24, 25, 26
                41, 2, 3, 4, 5, 8, 9, 10
                514, 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DPCADDPCADSFADDSFA
                案例371.5040.0025.506.00
                案例468.0048.7535.0026.25
                下載: 導出CSV
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