CIESC Journal ›› 2020, Vol. 71 ›› Issue (7): 3172-3179.DOI: 10.11949/0438-1157.20191581
• Process system engineering • Previous Articles Next Articles
Xuejin GAO1,2,3,4(),Tengfei LIU1,2,3,4,Zidong XU1,2,3,4,Huihui GAO1,2,3,4,Yongchuan YU1,2,3,4()
Received:
2019-12-25
Revised:
2020-02-05
Online:
2020-07-05
Published:
2020-07-05
Contact:
Yongchuan YU
高学金1,2,3,4(),刘腾飞1,2,3,4,徐子东1,2,3,4,高慧慧1,2,3,4,于涌川1,2,3,4()
通讯作者:
于涌川
作者简介:
高学金(1973—),男,博士,教授, 基金资助:
CLC Number:
Xuejin GAO, Tengfei LIU, Zidong XU, Huihui GAO, Yongchuan YU. Intermittent process fault monitoring based on recurrent autoencoder[J]. CIESC Journal, 2020, 71(7): 3172-3179.
高学金, 刘腾飞, 徐子东, 高慧慧, 于涌川. 基于循环自动编码器的间歇过程故障监测[J]. 化工学报, 2020, 71(7): 3172-3179.
编号 | 名称 | 编号 | 名称 |
---|---|---|---|
x 1 | 通风速率/(L/h) | x 6 | 排气CO2浓度/(mmol/L) |
x 2 | 搅拌功率/W | x 7 | pH |
x 3 | 底物流加速率/(L/h) | x 8 | 反应温度/K |
x 4 | 补料温度/K | x 9 | 反应热/J |
x 5 | 溶解氧浓度/(mmol/L) | x 10 | 冷水流加速率/(L/h) |
Table 1 The main variables of penicillin fermentation process
编号 | 名称 | 编号 | 名称 |
---|---|---|---|
x 1 | 通风速率/(L/h) | x 6 | 排气CO2浓度/(mmol/L) |
x 2 | 搅拌功率/W | x 7 | pH |
x 3 | 底物流加速率/(L/h) | x 8 | 反应温度/K |
x 4 | 补料温度/K | x 9 | 反应热/J |
x 5 | 溶解氧浓度/(mmol/L) | x 10 | 冷水流加速率/(L/h) |
故障 | 故障变量 | 故障类型 | 幅值/% | 持续时间/h |
---|---|---|---|---|
1 | 通风速率 | 阶跃故障 | 5 | 200~400 |
2 | 底物流加速率 | 斜坡故障 | 5 | 200~400 |
3 | 搅拌功率 | 斜坡故障 | 1 | 150~400 |
Table 2 Fault batch settings
故障 | 故障变量 | 故障类型 | 幅值/% | 持续时间/h |
---|---|---|---|---|
1 | 通风速率 | 阶跃故障 | 5 | 200~400 |
2 | 底物流加速率 | 斜坡故障 | 5 | 200~400 |
3 | 搅拌功率 | 斜坡故障 | 1 | 150~400 |
隐层节点数 | d=2 | d=4 | d=8 | d=16 | d=32 |
---|---|---|---|---|---|
16 | 4.135 | 3.866 | 3.695 | 3.411 | 2.978 |
32 | 2.438 | 2.054 | 1.891 | 1.979 | 2.103 |
64 | 0.437 | 0.173 | 0.079 | 0.186 | 0.315 |
128 | 0.824 | 0.745 | 0.542 | 0.475 | 0.633 |
Table 3 Mean reconstruction error values of models with different parameters
隐层节点数 | d=2 | d=4 | d=8 | d=16 | d=32 |
---|---|---|---|---|---|
16 | 4.135 | 3.866 | 3.695 | 3.411 | 2.978 |
32 | 2.438 | 2.054 | 1.891 | 1.979 | 2.103 |
64 | 0.437 | 0.173 | 0.079 | 0.186 | 0.315 |
128 | 0.824 | 0.745 | 0.542 | 0.475 | 0.633 |
Fault | BDKPCA | AE | MWAE | RAE |
---|---|---|---|---|
1 | 100 | 100 | 100 | 100 |
2 | 94.5 | 90 | 93 | 96.5 |
3 | 89.3 | 74 | 81 | 90.4 |
Table 4 Fault detection rate of four methods/%
Fault | BDKPCA | AE | MWAE | RAE |
---|---|---|---|---|
1 | 100 | 100 | 100 | 100 |
2 | 94.5 | 90 | 93 | 96.5 |
3 | 89.3 | 74 | 81 | 90.4 |
Fault | BDKPCA | AE | MWAE | RAE |
---|---|---|---|---|
1 | 0 | 4.5 | 1.5 | 0.5 |
2 | 9 | 5 | 2.5 | 0 |
3 | 15.6 | 6 | 3.3 | 1.3 |
Table 5 False alarm rate of four methods/%
Fault | BDKPCA | AE | MWAE | RAE |
---|---|---|---|---|
1 | 0 | 4.5 | 1.5 | 0.5 |
2 | 9 | 5 | 2.5 | 0 |
3 | 15.6 | 6 | 3.3 | 1.3 |
编号 | 名称 | 编号 | 名称 |
---|---|---|---|
x 1 | 温度/oC | x 5 | 溶解氧浓度/% |
x 2 | 搅拌转速/(r/min) | x 6 | 罐内pH |
x 3 | 通气量/(L/min) | x 7 | 罐外pH |
x 4 | 罐压/Pa |
Table 6 The main variables of recombinant Escherichia coli fermentation process
编号 | 名称 | 编号 | 名称 |
---|---|---|---|
x 1 | 温度/oC | x 5 | 溶解氧浓度/% |
x 2 | 搅拌转速/(r/min) | x 6 | 罐内pH |
x 3 | 通气量/(L/min) | x 7 | 罐外pH |
x 4 | 罐压/Pa |
14 | Jia M X , Chu F , Wang F L , et al . On-line batch process monitoring using batch dynamic kernel principal component analysis[J]. Chemometrics and Intelligent Laboratory Systems, 2010, 101: 110-122. |
15 | Zhang Y W , Li S , Teng Y D . Dynamic processes monitoring using recursive kernel principal component analysis[J]. Chemical Engineering Science, 2012, 72: 78-86. |
16 | Zhang H , Tian X , Deng X . Batch process monitoring based on multiway global preserving kernel slow feature analysis[J]. IEEE Access, 2017, 5: 2696-2710. |
17 | 徐圆, 张伟, 张明卿, 等 . 基于 FEEMD-AE 与反馈极限学习机组合模型预测研究与应用[J]. 化工学报, 2018, 69(3):1064-1070. |
Xu Y , Zhang W , Zhang M Q , et al . Presiction research and application of a combination model based on FEEMD-AE and feedback extreme learning machine[J]. CIESC Journal, 2018, 69(3):1064-1070. | |
18 | Yan W , Guo P , Liang G , et al . Nonlinear and robust statistical process monitoring based on variant autoencoders[J]. Chemometrics & Intelligent Laboratory Systems, 2016, 158: 31-40. |
19 | 窦珊, 张广宇, 熊智华 . 基于LSTM时间序列重建的生产装置异常检测[J]. 化工学报, 2019, 70(2): 481-486. |
Dou S , Zhang G Y , Xiong Z H . Anomaly detection of process unit based on LSTM time series reconstruction[J]. CIESC Journal, 2019, 70(2): 481-486. | |
20 | Greff K , Srivastava R K , Koutník J , et al . LSTM: a search space odyssey[J]. IEEE Transactions on Neural Networks and Learning Systems, 2015, 28(10): 2222. |
21 | Agudo D , Ferrer A , Ferrer J , et al . Multivariate SPC of a sequencing batch reactor for wastewater treatment[J]. Chemometrics and Intelligent Laboratory Systems, 2007, 85(1): 82-93. |
22 | Bengio Y , Courville A , Vincent P . Representation learning: a review and new perspectives[J]. IEEE Transactions on Pattern Analysis & Machine Intelligence, 2012, 35(8): 1798-1828. |
23 | Hochreiter S , Schmidhuber J . Long short-term memory[J]. Neural Computation, 1997, 9(8): 1735-1780. |
1 | Kerkhof P V D , Gins G , Vanlaer J , et al . Dynamic model-based fault diagnosis for (bio)chemical batch processes[J]. Computers & Chemical Engineering, 2012, 40(1): 12-21. |
2 | Nomikos P , Macgregor J F . Monitoring batch processes using multiway principal component analysis[J]. AIChE Journal, 1994, 40(8): 1361-1375. |
3 | Nomikos P , Macgregor J F . Multiway partial least squares in monitoring batch progresses[J]. Chemometrics and Intelligent Laboratory Systems, 1995, 30(1): 97-108. |
4 | Liu Y , Liang Y , Gao Z , et al . Online flooding supervision in packed towers: an integrated data-driven statistical monitoring method[J]. Chemical Engineering and Technology, 2018, 41(3): 436-446. |
5 | 胡益, 王丽, 马贺贺, 等 . 基于核PLS方法的非线性过程在线监控[J]. 化工学报, 2011, 62(9): 2555-2561. |
Hu Y , Wang L , Ma H H , et al . Online nonlinear process monitoring using kernel partial least squares[J]. CIESC Journal, 2011, 62(9): 2555-2561. | |
6 | Qin Y , Zhao C H , Wang X Z , et al . Subspace decomposition and critical phase selection based cumulative quality analysis for multiphase batch processes [J]. Chemical Engineering Science, 2017, 166: 130-143. |
7 | 常鹏, 王普, 高学金 . 基于统计量模式分析的T-KPLS 间歇过程故障监控[J]. 化工学报, 2015, 66(1): 265-271. |
Chang P , Wang P , Gao X J . Fault monitoring batch process based on statistics pattern analysis of T-KPLS[J]. CIESC Journal, 2015, 66(1): 265-271. | |
8 | 赵春晖, 王福利, 姚远, 等 . 基于时段的间歇过程统计建模、在线监测及质量预报[J]. 自动化学报, 2010, 36(3): 366-374. |
Zhao C H , Wang F L , Yao Y , et al . Phase-based statistical modeling, online monitoring and quality prediction for batch processes[J]. Acta Automatica Sinica, 2010, 36(3): 366-374. | |
9 | Zhou Z T , Zhong M Y , Wang Y Q . Fault diagnosis observer and fault-tolerant control design for unmanned surface vehicles in network environments[J]. IEEE Access, 2019, 7: 173694-173702. |
10 | 常鹏, 乔俊飞, 王普, 等 . 基于MKECA的非高斯性和非线性共存的间歇过程监测[J]. 化工学报, 2018, 69(3): 1200-1206. |
Chang P , Qiao J F , Wang P , et al . Montoring non-Gaussian and non-linear batch process based on multi-way kernel entropy component analysis[J]. CIESC Journal, 2018, 69(3): 1200-1206. | |
11 | Fan J , Qin S J , Wang Y . Online monitoring of nonlinear multivariate industrial processes using filtering KICA-PCA[J]. Control Engineering Practice, 2014, 22: 205-216. |
12 | Liu Y , Yang C , Gao Z L , et al . Ensemble deep kernel learning with application to quality prediction in industrial polymerization processes[J]. Chemometrics and Intelligent Laboratory Systems, 2018, 174: 15-21. |
13 | Wang T , Wang X , Zhang Y , et al . Fault detection of nonlinear dynamic processes using dynamic kernel principal component analysis[C]// 7th World Congress on Intelligent Control and Automation. 2008: 3009-3014. |
24 | Chen Q , Wynne R J , Goulding P , et al . The application of principal compontent analysis and kernel density estimation to enhance process monitoring[J]. Control Engineering Practice, 2000, 8(5): 531-543. |
25 | Li G , Qin S J . Comparative study on monitoring schemes for non-Gaussian distributed processes[J]. Journal of Process Control, 2018, 67: 69-82. |
26 | Vanlaer J , Gins G , van Impe J F M . Quality assessment of a variance estimator for partial least squares prediction of batch-end quality[J]. Computers & Chemical Engineering, 2013, 52: 230-239. |
27 | 胡永兵, 高学金, 李亚芬, 等 . 基于仿射传播聚类子集主元分析的间歇过程监测方法[J]. 化工学报, 2016, 67(5): 1989-1997. |
Hu Y B , Gao X J , Li Y F , et al . Subset multiway principal component analysis monitoring for batch process based on affinity propagation clustering[J]. CIESC Journal, 2016, 67(5): 1989-1997. | |
28 | Lu H , Plataniotis K N , Venetsanopoulos A N . MPCA: multilinear principal component analysis of tensor objects[J]. Neural Networks, IEEE Transactions on, 2008, 19(1): 18-39. |
29 | Lv Z , Yan X , Jiang Q . Batch process monitoring based on just-in-time learning and multiple-subspace principal component analysis[J]. Chemometrics and Intelligent Laboratory Systems, 2014, 137: 128-139. |
30 | Birol G , Undey C , Cinar A . A modular simulation package for fed-batch fermentation: penicillin production[J]. Computers and Chemical Engineering, 2002, 26(11): 1553-1565. |
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