化工学报 ›› 2022, Vol. 73 ›› Issue (7): 3109-3119.DOI: 10.11949/0438-1157.20220210
收稿日期:
2022-02-15
修回日期:
2022-04-06
出版日期:
2022-07-05
发布日期:
2022-08-01
通讯作者:
侍洪波
作者简介:
王琨(1996—),女,博士研究生,基金资助:
Kun WANG(),Hongbo SHI(),Shuai TAN,Bing SONG,Yang TAO
Received:
2022-02-15
Revised:
2022-04-06
Online:
2022-07-05
Published:
2022-08-01
Contact:
Hongbo SHI
摘要:
传统的邻域保持嵌入(neighborhood preserving embedding,NPE)算法通过k近邻(k-nearest neighbors,k-NN)方法选择邻域进行重构来实现降维。但在实际工业过程中采集的样本具有时序相关性,仅仅通过欧氏距离选择近邻样本不能充分反映数据中包含的信息,从而影响检测效果。因此,提出一种局部时差约束邻域保持嵌入(local time difference constrained neighborhood preserving embedding,LTDCNPE)算法,充分考虑样本间的时间和空间关系,从而建立准确的故障检测模型。首先,该算法在固定尺度的时间窗内,根据样本的时序关系和空间特征挑选出邻域。其次,利用样本间的时间差异为邻域样本进行加权,使数据特征保留了高维空间的局部结构。然后,对降维后得到的主元空间和残差空间构建
中图分类号:
王琨, 侍洪波, 谭帅, 宋冰, 陶阳. 局部时差约束邻域保持嵌入算法在故障检测中的应用[J]. 化工学报, 2022, 73(7): 3109-3119.
Kun WANG, Hongbo SHI, Shuai TAN, Bing SONG, Yang TAO. Local time difference constrained neighborhood preserving embedding algorithm for fault detection[J]. CIESC Journal, 2022, 73(7): 3109-3119.
故障 | 描述 |
---|---|
1 | 对 |
2 | 系数矩阵 |
表1 过程故障描述
Table 1 Process fault description
故障 | 描述 |
---|---|
1 | 对 |
2 | 系数矩阵 |
Fault | MAR/% | |||||||
---|---|---|---|---|---|---|---|---|
PCA | NPE | DNPE | LTDCNPE | |||||
SPE | SPE | SPE | SPE | |||||
1 | 58.67 | 1.33 | 62.00 | 2.00 | 1.00 | 1.32 | 0.33 | 2.00 |
2 | 1.67 | 1.67 | 1.67 | 1.67 | 1.66 | 1.66 | 1.39 | 1.67 |
表2 数值例子的漏报率
Table 2 MAR in case study
Fault | MAR/% | |||||||
---|---|---|---|---|---|---|---|---|
PCA | NPE | DNPE | LTDCNPE | |||||
SPE | SPE | SPE | SPE | |||||
1 | 58.67 | 1.33 | 62.00 | 2.00 | 1.00 | 1.32 | 0.33 | 2.00 |
2 | 1.67 | 1.67 | 1.67 | 1.67 | 1.66 | 1.66 | 1.39 | 1.67 |
Fault | MAR(FAR)/% | ||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|
PCA | NPE | DNPE | LTDCNPE | ||||||||
SPE | SPE | SPE | SPE | ||||||||
1 | 0.88(0) | 0.13(0.63) | 0.88(0) | 0.75(0) | 0.13(0) | 0.50(0) | 0.25(1.25) | 0.75(0) | |||
2 | 1.63(1.25) | 4(1.25) | 1.63(1.25) | 1.75(0) | 1.25(0) | 1.75(0) | 1.50(0) | 1.75(0) | |||
5 | 75.88(0.63) | 75.88(3.13) | 76.25(0.63) | 75.38(0.63) | 0(1.25) | 76.32(0.63) | 0(0) | 77.25(0.63) | |||
6 | 0.88(0) | 0(1.88) | 0.75(0.63) | 0(0) | 0(1.88) | 0(0.63) | 0(0) | 0(0) | |||
7 | 0(0) | 0(2.50) | 0(0) | 0(0) | 0(1.25) | 0(1.25) | 0(0.63) | 0(0) | |||
8 | 3.13(0) | 13.88(0.63) | 3.25(0) | 2.50(0) | 2.26(0) | 2.51(0) | 2.25(0) | 2.50(0) | |||
10 | 70.38(0) | 70.88(1.25) | 70.63(0) | 60.63(0) | 46.49(0.63) | 61.40(0) | 12(1.25) | 61.13(0) | |||
11 | 59.38(0.63) | 23.88(3.13) | 59.25(0.63) | 45.50(0.63) | 57.39(0.63) | 42.61(0) | 38.13(0.63) | 45.50(0.63) | |||
12 | 1.63(0) | 9.25(3.13) | 1.63(0.63) | 1.63(0) | 0.38(0) | 1.00(0) | 0.13(1.88) | 1.63(0) | |||
13 | 6.38(0.63) | 4.75(1.25) | 6.25(0) | 5.75(0) | 5.51(0) | 5.64(0) | 4.75(0.63) | 5.75(0) | |||
14 | 0.75(0) | 0(1.25) | 1.25(0.63) | 0.13(0) | 0(0.63) | 0(0.63) | 0(0.63) | 0.13(0) | |||
16 | 86.50(3.75) | 67.75(2.50) | 84.88(3.13) | 78.75(5.63) | 55.26(1.88) | 81.20(1.88) | 8.88(7.50) | 79.25(5.63) | |||
17 | 23.75(1.25) | 4.13(2.50) | 24.50(1.88) | 14.13(0) | 14.29(0) | 14.29(0) | 9.13(0) | 14.13(0) | |||
18 | 10.75(0) | 9.75(2.50) | 10.63(0) | 10.75(0) | 10.78(0.63) | 10.65(0) | 9.63(0.63) | 10.75(0) | |||
19 | 89.00(0) | 82.25(0.63) | 88.38(0) | 98.13(0) | 71.43(0) | 100(0) | 22.00(0.63) | 98.13(0) | |||
20 | 68.25(0) | 48.38(4.38) | 65.13(0) | 57.88(0) | 50.50(0) | 58.90(0) | 11.00(0) | 58.38(0) | |||
21 | 60.75(0) | 51.13(5.00) | 60.50(0) | 61.75(0) | 51.13(0.63) | 62.91(0) | 42.00(3.13) | 61.75(0) |
表3 TE过程17种故障的漏报率和误报率
Table 3 MAR and FAR of 17 faults in TE process
Fault | MAR(FAR)/% | ||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|
PCA | NPE | DNPE | LTDCNPE | ||||||||
SPE | SPE | SPE | SPE | ||||||||
1 | 0.88(0) | 0.13(0.63) | 0.88(0) | 0.75(0) | 0.13(0) | 0.50(0) | 0.25(1.25) | 0.75(0) | |||
2 | 1.63(1.25) | 4(1.25) | 1.63(1.25) | 1.75(0) | 1.25(0) | 1.75(0) | 1.50(0) | 1.75(0) | |||
5 | 75.88(0.63) | 75.88(3.13) | 76.25(0.63) | 75.38(0.63) | 0(1.25) | 76.32(0.63) | 0(0) | 77.25(0.63) | |||
6 | 0.88(0) | 0(1.88) | 0.75(0.63) | 0(0) | 0(1.88) | 0(0.63) | 0(0) | 0(0) | |||
7 | 0(0) | 0(2.50) | 0(0) | 0(0) | 0(1.25) | 0(1.25) | 0(0.63) | 0(0) | |||
8 | 3.13(0) | 13.88(0.63) | 3.25(0) | 2.50(0) | 2.26(0) | 2.51(0) | 2.25(0) | 2.50(0) | |||
10 | 70.38(0) | 70.88(1.25) | 70.63(0) | 60.63(0) | 46.49(0.63) | 61.40(0) | 12(1.25) | 61.13(0) | |||
11 | 59.38(0.63) | 23.88(3.13) | 59.25(0.63) | 45.50(0.63) | 57.39(0.63) | 42.61(0) | 38.13(0.63) | 45.50(0.63) | |||
12 | 1.63(0) | 9.25(3.13) | 1.63(0.63) | 1.63(0) | 0.38(0) | 1.00(0) | 0.13(1.88) | 1.63(0) | |||
13 | 6.38(0.63) | 4.75(1.25) | 6.25(0) | 5.75(0) | 5.51(0) | 5.64(0) | 4.75(0.63) | 5.75(0) | |||
14 | 0.75(0) | 0(1.25) | 1.25(0.63) | 0.13(0) | 0(0.63) | 0(0.63) | 0(0.63) | 0.13(0) | |||
16 | 86.50(3.75) | 67.75(2.50) | 84.88(3.13) | 78.75(5.63) | 55.26(1.88) | 81.20(1.88) | 8.88(7.50) | 79.25(5.63) | |||
17 | 23.75(1.25) | 4.13(2.50) | 24.50(1.88) | 14.13(0) | 14.29(0) | 14.29(0) | 9.13(0) | 14.13(0) | |||
18 | 10.75(0) | 9.75(2.50) | 10.63(0) | 10.75(0) | 10.78(0.63) | 10.65(0) | 9.63(0.63) | 10.75(0) | |||
19 | 89.00(0) | 82.25(0.63) | 88.38(0) | 98.13(0) | 71.43(0) | 100(0) | 22.00(0.63) | 98.13(0) | |||
20 | 68.25(0) | 48.38(4.38) | 65.13(0) | 57.88(0) | 50.50(0) | 58.90(0) | 11.00(0) | 58.38(0) | |||
21 | 60.75(0) | 51.13(5.00) | 60.50(0) | 61.75(0) | 51.13(0.63) | 62.91(0) | 42.00(3.13) | 61.75(0) |
1 | Liu J X, Chen J H, Wang D. Linear and exponential fault-assistant feature extraction methods for process monitoring[J]. Control Engineering Practice, 2021, 109(3): 104732. |
2 | 彭开香, 张传放, 马亮, 等. 面向系统层级的复杂工业过程全息故障诊断[J]. 化工学报, 2019, 70(2): 590-598. |
Peng K X, Zhang C F, Ma L, et al. System-levels-based holographic fault diagnosis for complex industrial processes[J]. CIESC Journal, 2019, 70(2): 590-598. | |
3 | Yao L, Ge Z Q. Refining data-driven soft sensor modeling framework with variable time reconstruction[J]. Journal of Process Control, 2020, 87: 91-107. |
4 | 蓝艇, 童楚东, 史旭华. 变量加权型主元分析算法及其在故障检测中的应用[J]. 化工学报, 2017, 68(8): 3177-3182. |
Lan T, Tong C D, Shi X H. Variable weighted principal component analysis algorithm and its application in fault detection[J]. CIESC Journal, 2017, 68(8): 3177-3182. | |
5 | Qin S J, Chiang L H. Advances and opportunities in machine learning for process data analytics[J]. Computers & Chemical Engineering, 2019, 126: 465-473. |
6 | Chang Y Q, Ma R X, Zhao L P, et al. Online operating performance evaluation for the plant-wide industrial process based on a three-level and multi-block method[J]. The Canadian Journal of Chemical Engineering, 2019, 97(S1): 1371-1385. |
7 | Song B, Shi H B, Tan S, et al. Multisubspace orthogonal canonical correlation analysis for quality-related plant-wide process monitoring[J]. IEEE Transactions on Industrial Informatics, 2021, 17(9): 6368-6378. |
8 | Tao Y, Shi H B, Song B, et al. A novel dynamic weight principal component analysis method and hierarchical monitoring strategy for process fault detection and diagnosis[J]. IEEE Transactions on Industrial Electronics, 2020, 67(9): 7994-8004. |
9 | Zhou P, Zhang R Y, Liang M Y, et al. Fault identification for quality monitoring of molten iron in blast furnace ironmaking based on KPLS with improved contribution rate[J]. Control Engineering Practice, 2020, 97: 104354. |
10 | Li Z C, Yan X F. Fault-relevant optimal ensemble ICA model for non-Gaussian process monitoring[J]. IEEE Transactions on Control Systems Technology, 2019, 28(6): 2581-2590. |
11 | 常玉清, 王姝, 王福利, 等. 基于多PCA模型的过程监测方法[J]. 仪器仪表学报, 2014, 35(4): 901-908. |
Chang Y Q, Wang S, Wang F L, et al. Process monitoring method based on multiple PCA models[J]. Chinese Journal of Scientific Instrument, 2014, 35(4): 901-908. | |
12 | Talmon R, Mallat S, Zaveri H, et al. Manifold learning for latent variable inference in dynamical systems[J]. IEEE Transactions on Signal Processing, 2015, 63(15): 3843-3856. |
13 | Belkin M, Niyogi P. Laplacian eigenmaps and spectral techniques for embedding and clustering[M]//Advances in Neural Information Processing Systems. Massachusetts, USA: The MIT Press, 2002. |
14 | Roweis S T, Saul L K. Nonlinear dimensionality reduction by locally linear embedding[J]. Science, 2000, 290(5500): 2323-2326. |
15 | Tenenbaum J B, Silva V D, Langford J C. A global geometric framework for nonlinear dimensionality reduction[J]. Science, 2000, 290(5500): 2319-2323. |
16 | He X F, Niyogi P. Locality preserving projections[J]. Advances in Neural Information Processing Systems, 2004, 16(16): 153-160. |
17 | He X F, Cai D, Yan S C, et al. Neighborhood preserving embedding[C]//Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1. Beijing: IEEE, 2005: 1208-1213. |
18 | Miao A M, Ge Z Q, Song Z H, et al. Nonlocal structure constrained neighborhood preserving embedding model and its application for fault detection[J]. Chemometrics and Intelligent Laboratory Systems, 2015, 142: 184-196. |
19 | Huang P F, Tao Y, Song B, et al. Tensor sequence component analysis for fault detection in dynamic process[J]. The Canadian Journal of Chemical Engineering, 2020, 98(1): 225-236. |
20 | Song B, Tan S, Shi H B. Process monitoring via enhanced neighborhood preserving embedding[J]. Control Engineering Practice, 2016, 50: 48-56. |
21 | Tong C D, Lan T, Shi X H, et al. Statistical process monitoring based on nonlocal and multiple neighborhoods preserving embedding model[J]. Journal of Process Control, 2018, 65: 34-40. |
22 | Ge Z Q, Chen X R. Dynamic probabilistic latent variable model for process data modeling and regression application[J]. IEEE Transactions on Control Systems Technology, 2019, 27(1): 323-331. |
23 | Ku W F, Storer R H, Georgakis C. Disturbance detection and isolation by dynamic principal component analysis[J]. Chemometrics and Intelligent Laboratory Systems, 1995, 30(1): 179-196. |
24 | Li Y, Bi Y, Sun J, et al. Multi-class evaluation using dynamic neighborhood preserving embedding method[J]. Journal of Computational Information Systems, 2015, 11(19): 7001-7006. |
25 | 赵小强, 牟淼. 基于GSFA-GNPE的动态-静态联合指标间歇过程监控[J]. 上海交通大学学报, 2021, 55(11): 1417-1428. |
Zhao X Q, Mou M. Batch process monitoring with dynamic-static joint indicator based on GSFA-GNPE[J]. Journal of Shanghai Jiao Tong University, 2021, 55(11): 1417-1428. | |
26 | 杨健, 宋冰, 谭帅, 等. 时序约束NPE算法在化工过程故障检测中的应用[J]. 化工学报, 2016, 67(12): 5131-5139. |
Yang J, Song B, Tan S, et al. Time constrained NPE for fault detection in chemical processes[J]. CIESC Journal, 2016, 67(12): 5131-5139. | |
27 | Odiowei P P, Cao Y. Nonlinear dynamic process monitoring using canonical variate analysis and kernel density estimations[J]. Computer Aided Chemical Engineering, 2009, 27: 1557-1562. |
28 | Chen Q, Wynne R J, Goulding P, et al. The application of principal component analysis and kernel density estimation to enhance process monitoring[J]. Control Engineering Practice, 2000, 8(5): 531-543. |
29 | Liang J. Multivariate statistical process monitoring using kernel density estimation[J]. Developments in Chemical Engineering and Mineral Processing, 2008, 13(1/2): 185-192. |
30 | Song B, Ma Y X, Shi H B. Multimode process monitoring using improved dynamic neighborhood preserving embedding[J]. Chemometrics and Intelligent Laboratory Systems, 2014, 135:17-30. |
31 | Zhao X Q, Wang T. Tensor dynamic neighborhood preserving embedding algorithm for fault diagnosis of batch process[J]. Chemometrics and Intelligent Laboratory Systems, 2017, 162: 94-103. |
32 | Bathelt A, Ricker N L, Jelali M. Revision of the Tennessee Eastman process model[J]. IFAC-PapersOnLine, 2015, 48(8): 309-314. |
33 | Rong M Y, Shi H B, Tan S. Large-scale supervised process monitoring based on distributed modified principal component regression[J]. Industrial & Engineering Chemistry Research, 2019, 58(39): 18223-18240. |
34 | Lyman P R, Georgakis C. Plant-wide control of the Tennessee Eastman problem[J]. Computers & Chemical Engineering, 1995, 19(3): 321-331. |
35 | Yu J B. Local and global principal component analysis for process monitoring[J]. Journal of Process Control, 2012, 22(7): 1358-1373. |
36 | Saul L, Roweis S. Think globally, fit locally: unsupervised learning of low dimensional manifold[J]. Journal of Machine Learning Research, 2003, 4: 119-155. |
37 | 蒋浩天, E.L. 拉塞尔, R.D. 布拉茨, 等. 工业系统的故障检测与诊断[M]. 段建民, 译. 北京: 机械工业出版社, 2003. |
Chiang L H, Russell E L, Braatz R D, et al. Fault Detection and Diagnosis in Industrial Systems[M]. Duan J M, trans. Beijing: China Machine Press, 2003. |
[1] | 张逸豪, 王振雷. 基于最大信息系数的分组支持向量数据描述故障检测[J]. 化工学报, 2023, 74(9): 3865-3878. |
[2] | 康飞, 吕伟光, 巨锋, 孙峙. 废锂离子电池放电路径与评价研究[J]. 化工学报, 2023, 74(9): 3903-3911. |
[3] | 陈哲文, 魏俊杰, 张玉明. 超临界水煤气化耦合SOFC发电系统集成及其能量转化机制[J]. 化工学报, 2023, 74(9): 3888-3902. |
[4] | 曹跃, 余冲, 李智, 杨明磊. 工业数据驱动的加氢裂化装置多工况切换过渡状态检测[J]. 化工学报, 2023, 74(9): 3841-3854. |
[5] | 郑玉圆, 葛志伟, 韩翔宇, 王亮, 陈海生. 中高温钙基材料热化学储热的研究进展与展望[J]. 化工学报, 2023, 74(8): 3171-3192. |
[6] | 李贵贤, 曹阿波, 孟文亮, 王东亮, 杨勇, 周怀荣. 耦合固体氧化物电解槽的CO2制甲醇过程设计与评价研究[J]. 化工学报, 2023, 74(7): 2999-3009. |
[7] | 邵远哲, 赵忠盖, 刘飞. 基于共同趋势模型的非平稳过程质量相关故障检测方法[J]. 化工学报, 2023, 74(6): 2522-2537. |
[8] | 邵伟明, 韩文学, 宋伟, 杨勇, 陈灿, 赵东亚. 基于分布式贝叶斯隐马尔可夫回归的动态软测量建模方法[J]. 化工学报, 2023, 74(6): 2495-2502. |
[9] | 苏晓丹, 朱干宇, 李会泉, 郑光明, 孟子衡, 李防, 杨云瑞, 习本军, 崔玉. 湿法磷酸半水工艺考察与石膏结晶过程研究[J]. 化工学报, 2023, 74(4): 1805-1817. |
[10] | 贠程, 王倩琳, 陈锋, 张鑫, 窦站, 颜廷俊. 基于社团结构的化工过程风险演化路径深度挖掘[J]. 化工学报, 2023, 74(4): 1639-1650. |
[11] | 宋冰, 郑城风, 侍洪波, 陶阳, 谭帅. 基于VAE-OCCA的质量相关故障检测方法研究[J]. 化工学报, 2023, 74(4): 1630-1638. |
[12] | 王子宗, 索寒生, 赵学良. 数字孪生智能乙烯工厂研究与构建[J]. 化工学报, 2023, 74(3): 1175-1186. |
[13] | 张中秋, 李宏光, 石逸林. 基于人工预测调控策略的复杂化工过程多任务学习方法[J]. 化工学报, 2023, 74(3): 1195-1204. |
[14] | 张江淮, 赵众. 碳三加氢装置鲁棒最小协方差约束控制及应用[J]. 化工学报, 2023, 74(3): 1216-1227. |
[15] | 雍加望, 赵倩倩, 冯能莲. 基于非线性动态模型的质子交换膜燃料电池故障诊断[J]. 化工学报, 2022, 73(9): 3983-3993. |
阅读次数 | ||||||||||||||||||||||||||||||||||||||||||||||||||
全文 108
|
|
|||||||||||||||||||||||||||||||||||||||||||||||||
摘要 237
|
|
|||||||||||||||||||||||||||||||||||||||||||||||||